Systems and methods for PET image reconstruction

ABSTRACT

A method may include: obtaining a 3D CT image of a scanning area of a subject; obtaining PET data of the scanning area of the subject; gating the PET data based on a plurality of motion phases; reconstructing a plurality of gated 3D PET images; registering the plurality of gated 3D PET images with a reference 3D PET image; determining a motion vector field corresponding to a gated 3D PET image of the plurality of gated 3D PET images based on the registration; determining a motion phase for each of the plurality of CT image layers; correcting, for each of the plurality of CT image layers, the CT image layer with respect to a reference motion phase; and reconstructing a gated PET image with respect to the reference motion phase based on the corrected CT image layers and the PET data.

CROSS-REFERENCE TO RELATED APPLICATIONS

This application is a Continuation of U.S. patent application Ser. No.15/645,558, filed on Jul. 10, 2017, the entire contents of which arehereby incorporated by reference.

TECHNICAL FIELD

The present disclosure generally relates to systems and methods forimage processing, and more specifically relates to methods and systemsfor reconstructing PET image.

BACKGROUND

Positron emission tomography (PET) is a specialized radiology procedurethat generates three-dimensional images of functional processes in atarget organ or tissue of a body. Specifically, in PET studies, abiologically active molecule carrying a radioactive tracer is firstintroduced into a subject's body. The PET system then detects pairs ofgamma rays emitted indirectly by the tracer and reconstructs athree-dimensional image of the tracer concentration within the body byanalyzing the detected signals. Because the biologically activemolecules used in PET studies are natural substrates of metabolism atthe target organ or tissue, PET can evaluate the physiology(functionality) of the target organ or tissue, as well as itsbiochemical properties. Changes in these properties of the target organor tissue may provide information for the identification of the onset orprogression of a disease before an anatomical change relating to thedisease become detectable by other diagnostic tests, such as computedtomography (CT) or magnetic resonance imaging (MRI).

Furthermore, the high sensitivity of PET—in the picomolar range—mayallow the detection of small amounts of radio-labeled markers in vivo.PET may be used in conjunction with other diagnostic tests to achievesimultaneous acquisition of both structural and functional informationof the body of a subject. Examples include a PET/CT hybrid system, aPET/MR hybrid system.

A PET/CT image may be obtained using a PET/CT hybrid system. During ascan in the PET/CT system, a subject may undergo respiratory motion,which may cause artifact in an image. The PET data may be correctedbased on the CT data in order to compensate for the attenuation of thePET projection data caused by loss of detection of true coincidenceevents. A PET image may be obtained based on the corrected PET data. Tothis end, the CT data and the PET data may need to be matched withrespect to the scanning of a same area of a subject; a mismatch maysubsequently cause artifacts in the PET image, which in turn may affectan interpretation of the PET image, or diagnose on the basis of the PETimage. During a scanning of the subject by the PET/CT hybrid system, ifthe scanning is operated for chest or upper abdomen examinations,respiratory motion of the lungs and/or cardiac motion of the heart ofthe subject may lead to the mismatch. Thus, it is desirable to develop amethod and system for matching such acquired CT data and PET data toreduce the effect of respiratory and/or cardiac motion of the subjectand improve the quality of a PET image reconstructed accordingly.

SUMMARY

According to an aspect of the present disclosure, a method may include:obtaining a 3D CT image of a scanning area of a subject, the 3D CT imageincluding a plurality of CT image layers, a CT image layer correspondingto a group of spatial points relating to the subject; obtaining PET dataof the scanning area of the subject, the PET data corresponding to afirst motion signal with a plurality of motion phases of the subject;gating the PET data based on the plurality of motion phases of the firstmotion signal; reconstructing, based on the gated PET data, a pluralityof gated 3D PET images, a gated 3D PET image corresponding to one of theplurality of motion phases, a gated 3D PET image including a pluralityof gated PET image layers, a gated PET image layer corresponding to agroup of spatial points relating to the subject; registering theplurality of gated 3D PET images with a reference 3D PET image;determining, based on the registration, a motion vector fieldcorresponding to a gated 3D PET image of the plurality of gated 3D PETimages, a motion vector field corresponding to a motion phase;determining, for each of the plurality of CT image layers, a motionphase based on the motion phases of the plurality of gated 3D PETimages; correcting, for each of the plurality of CT image layers, basedon a motion vector field of a gated 3D PET image corresponding to thesame motion phase as the CT image layer with respect to a gated 3D PETimage corresponding to a reference motion phase, the CT image layer withrespect to the reference motion phase; and reconstructing a gated PETimage with respect to the reference motion phase based on the correctedCT image layers and the PET data.

In some embodiments, the determining the motion phase for each of theplurality of CT image layers based on the motion phases of the pluralityof gated 3D PET images may include: identifying, from each of theplurality of gated 3D PET image, a gated PET image layer correspondingto same group of spatial points as the CT image layer; determining asimilarity between the CT image layer and each of the plurality ofidentified gated PET image layers; and designating one of the motionphases of the plurality of gated 3D PET images as the motion phase ofthe CT image layer based on its similarities with the plurality ofidentified gated PET image layers.

In some embodiments, the designating one of the motion phases of theplurality of gated 3D PET images as the motion phase of the CT imagelayer based on its similarities with the plurality of identified gatedPET image layers may include: identifying a highest similarity among thedetermined similarities between the CT image layer and the plurality ofidentified gated PET image layers; and designating the motion phase ofthe gated 3D PET image including the identified gated PET image layerhaving the highest similarity as the motion phase of the CT image.

In some embodiments, the determining the similarity between the CT imagelayer and each of the plurality of identified gated PET image layers maybe based on at least one of a pixel-based similarity, an entropy-basedsimilarity, a mutual information similarity, and a contour-basedsimilarity.

In some embodiments, wherein the determining the motion phase for eachof the plurality of CT image layers based on the motion phases of theplurality of gated 3D PET images may include: obtaining a second motionsignal during a scanning that provides the 3D CT image, wherein thesecond motion signal is of a same type as the first motion signal; anddetermining the motion phase of the CT image layer based on the motionphases of the plurality of gated 3D PET images and the second motionsignal.

In some embodiments, the second motion signal may be obtained from anexternal device.

In some embodiments, the plurality of motion phases of the first motionsignal may be determined based on an amplitude or a time interval of amotion presented in the motion signal.

In some embodiments, the registering the plurality of gated 3D PETimages with a reference 3D PET image may be based on at least one of anoptical flow registration algorithm, demons registration algorithm, or aB-spline registration algorithm.

In some embodiments, the correcting, for each of the plurality of CTimage layers, based on a motion vector field of a gated 3D PET imagecorresponding to the same motion phase as the CT image layer withrespect to a gated 3D PET image corresponding to a reference motionphase, the CT image layer with respect to the reference motion phase mayinclude: determining a deformation vector field for the CT image layerbased on the motion vector field of the gated 3D PET image correspondingto the same motion phase as the CT image layer with respect to a gated3D PET image corresponding to a reference motion phase; and correctingthe CT image layer with respect to the reference motion phase based onthe deformation vector field.

In some embodiments, the reconstructing the gated PET image with respectto the reference motion phase based on the corrected CT image layers andthe PET data may include: determining an attenuation map based on thecorrected CT image layers; and reconstructing the gated PET image withrespect to the reference motion phase based on the attenuation map andthe PET data.

In some embodiments, the motion vector field may include a plurality ofmotion vectors, the motion vector representing a motion of a spatialpoint of the subject from a gated 3D PET image to another gated 3D PETimage.

In some embodiments, the plurality of motion phases of the first motionsignal may be determined based on an amplitude or a time interval of amotion presented in the first motion signal.

In some embodiments, a CT image layer of the plurality of CT imagelayers may be a transverse slice of the 3D CT image, and a gated PETimage layer may be a transverse slice of a gate 3D PET image.

In some embodiments, the reference motion phase may be one of theplurality of motion phases of the subject

According to another aspect of the present disclosure, a system mayinclude at least one processor; and storage for storing instructions,the instructions, when executed by the at least one processor, causingthe system to perform a method. The method may include: obtaining a 3DCT image of a scanning area of a subject, the 3D CT image including aplurality of CT image layers, a CT image layer corresponding to a groupof spatial points relating to the subject; obtaining PET data of thescanning area of the subject, the PET data corresponding to a firstmotion signal with a plurality of motion phases of the subject; gatingthe PET data based on the plurality of motion phases of the first motionsignal; reconstructing, based on the gated PET data, a plurality ofgated 3D PET images, a gated 3D PET image corresponding to one of theplurality of motion phases, a gated 3D PET image including a pluralityof gated PET image layers, a gated PET image layer corresponding to agroup of spatial points relating to the subject; registering theplurality of gated 3D PET images with a reference 3D PET image;determining, based on the registration, a motion vector fieldcorresponding to a gated 3D PET image of the plurality of gated 3D PETimages, a motion vector field corresponding to a motion phase;determining, for each of the plurality of CT image layers, a motionphase based on the motion phases of the plurality of gated 3D PETimages; correcting, for each of the plurality of CT image layers, basedon a motion vector field of a gated 3D PET image corresponding to thesame motion phase as the CT image layer with respect to a gated 3D PETimage corresponding to a reference motion phase, the CT image layer withrespect to the reference motion phase; and reconstructing a gated PETimage with respect to the reference motion phase based on the correctedCT image layers and the PET data.

In some embodiments, the determining the motion phase for each of theplurality of CT image layers based on the motion phases of the pluralityof gated 3D PET images may include: identifying, from each of theplurality of gated 3D PET image, a gated PET image layer correspondingto same group of spatial points as the CT image layer; determining asimilarity between the CT image layer and each of the plurality ofidentified gated PET image layers; and designating one of the motionphases of the plurality of gated 3D PET images as the motion phase ofthe CT image layer based on its similarities with the plurality ofidentified gated PET image layers; and the correcting, for each of theplurality of CT image layers, based on a motion vector field of a gated3D PET image corresponding to the same motion phase as the CT imagelayer with respect to a gated 3D PET image corresponding to a referencemotion phase, the CT image layer with respect to the reference motionphase may include: determining a deformation vector field for the CTimage layer based on the motion vector field of the gated 3D PET imagecorresponding to the same motion phase as the CT image layer withrespect to a gated 3D PET image corresponding to a reference motionphase; and correcting the CT image layer with respect to the referencemotion phase based on the deformation vector field.

In some embodiments, the designating one of the motion phases of theplurality of gated 3D PET images as the motion phase of the CT imagelayer based on its similarities with the plurality of identified gatedPET image layers may include: identifying a highest similarity among thedetermined similarities between the CT image layer and the plurality ofidentified gated PET image layers; and designating the motion phase ofthe gated 3D PET image including the identified gated PET image layerhaving the highest similarity as the motion phase of the CT image.

In some embodiments, the determining the motion phase for each of theplurality of CT image layers based on the motion phases of the pluralityof gated 3D PET images may include: obtaining a second motion signalduring a scanning that provides the 3D CT image, wherein the secondmotion signal is of a same type or can be transformed to a same type asthe first motion signal; and determining the motion phase of the CTimage layer based on the motion phases of the plurality of gated 3D PETimages and the second motion signal, wherein the first motion signal isobtained based on the PET data.

In some embodiments, the system may be further caused to superimpose agated 3D image and a corrected 3D CT image of a same motion phase toobtain a superimposed 3D image.

According to another aspect of the present disclosure, a method mayinclude: obtaining a 3D CT image of a scanning area of a subject, the 3DCT image including a plurality of CT image layers, a CT image layercorresponding to a group of spatial points relating to the subject;obtaining PET data of the scanning area of the subject, the PET datacorresponding to a first motion signal with a plurality of motion phasesof the subject; gating the PET data based on the plurality of motionphases of the first motion signal; reconstructing, based on the gatedPET data, a plurality of gated 3D PET images, a gated 3D PET imagecorresponding to one of the plurality of motion phases, a gated 3D PETimage including a plurality of gated PET image layers, a gated PET imagelayer corresponding to a group of spatial points relating to thesubject; registering the plurality of gated 3D PET images with areference 3D PET image; determining, based on the registration, a motionvector field corresponding to a gated 3D PET image of the plurality ofgated 3D PET images, a motion vector field corresponding to a motionphase; determining, for each of the plurality of CT image layers, amotion phase based on the motion phases of the plurality of gated 3D PETimages; and correcting, for each of the plurality of CT image layers,based on a motion vector field of a gated 3D PET image corresponding tothe same motion phase as the CT image layer with respect to a gated 3DPET image corresponding to a reference motion phase, the CT image layerwith respect to the reference motion phase.

According to another aspect of the present disclosure, a non-transitorystorage medium may include a set of instructions, wherein when executedby at least one processor, the set of instructions may direct the atleast one processor to perform acts of: According to an aspect of thepresent disclosure, a method may include: obtaining a 3D CT image of ascanning area of a subject, the 3D CT image including a plurality of CTimage layers, a CT image layer corresponding to a group of spatialpoints relating to the subject; obtaining PET data of the scanning areaof the subject, the PET data corresponding to a first motion signal witha plurality of motion phases of the subject; gating the PET data basedon the plurality of motion phases of the first motion signal;reconstructing, based on the gated PET data, a plurality of gated 3D PETimages, a gated 3D PET image corresponding to one of the plurality ofmotion phases, a gated 3D PET image including a plurality of gated PETimage layers, a gated PET image layer corresponding to a group ofspatial points relating to the subject; registering the plurality ofgated 3D PET images with a reference 3D PET image; determining, based onthe registration, a motion vector field corresponding to a gated 3D PETimage of the plurality of gated 3D PET images, a motion vector fieldcorresponding to a motion phase; determining, for each of the pluralityof CT image layers, a motion phase based on the motion phases of theplurality of gated 3D PET images; correcting, for each of the pluralityof CT image layers, based on a motion vector field of a gated 3D PETimage corresponding to the same motion phase as the CT image layer withrespect to a gated 3D PET image corresponding to a reference motionphase, the CT image layer with respect to the reference motion phase.

In some embodiments, the acts may further comprise reconstructing agated PET image with respect to the reference motion phase based on thecorrected CT image layers and the PET data.

According to another aspect of the present disclosure, a system mayinclude an acquisition module, and a processing module. The acquisitionmodule may be configured to obtain a 3D CT image of a scanning area of asubject, the 3D CT image including a plurality of CT image layers, a CTimage layer corresponding to a group of spatial points relating to thesubject, and obtain PET data of the scanning area of the subject, thePET data corresponding to a first motion signal with a plurality ofmotion phases of the subject. The processing module may include a gatingunit, a reconstruction unit, a registration unit, a motion vector fielddetermination unit, a motion phase determination unit, and a motiondeformation processing unit. The gating unit may be configured to gatingthe PET data based on the plurality of motion phases of the first motionsignal. The reconstruction unit may be configured to reconstruct, basedon the gated PET data, a plurality of gated 3D PET images, a gated 3DPET image corresponding to one of the plurality of motion phases, agated 3D PET image including a plurality of gated PET image layers, agated PET image layer corresponding to a group of spatial pointsrelating to the subject. The registration unit may be configured toregister the plurality of gated 3D PET images with a reference 3D PETimage. The motion vector field determination unit may be configured todetermine, based on the registration, a motion vector fieldcorresponding to a gated 3D PET image of the plurality of gated 3D PETimages, a motion vector field corresponding to a motion phase. Themotion phase determination unit may be configured to, for each of theplurality of CT image layers, determine a motion phase based on themotion phases of the plurality of gated 3D PET images. The motiondeformation processing unit may be configured to correct, for each ofthe plurality of CT image layers, based on a motion vector field of agated 3D PET image corresponding to the same motion phase as the CTimage layer with respect to a gated 3D PET image corresponding to areference motion phase, the CT image layer with respect to the referencemotion phase.

In some embodiments, the reconstruction unit may be configured toreconstruct a gated PET image with respect to the reference motion phasebased on the corrected CT image layers and the PET data.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is further described in terms of exemplaryembodiments. These exemplary embodiments are described in detail withreference to the drawings. These embodiments are non-limiting exemplaryembodiments, in which like reference numerals represent similarstructures throughout the several views of the drawings, and wherein:

FIG. 1 is a schematic diagram illustrating an exemplary imaging systemaccording to some embodiments of the present disclosure;

FIG. 2 is a schematic diagram illustrating exemplary hardware andsoftware components of a computing device on which data processingsystem, may be implemented according to some embodiments of the presentdisclosure;

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device on which a userterminal may be implemented according to some embodiments of the presentdisclosure;

FIG. 4 is a block diagram illustrating an exemplary data processingsystem according to some embodiments of the present disclosure;

FIG. 5 is a block diagram illustrating an exemplary processing moduleaccording to some embodiments of the present disclosure;

FIG. 6A and FIG. 6B illustrate flowcharts illustrating exemplaryprocesses for processing image data according to some embodiments of thepresent disclosure;

FIG. 7 is a flowchart illustrating an exemplary process for gating PETdata according to some embodiments of the present disclosure;

FIG. 8 is a flowchart illustrating an exemplary process for determininga respiratory phase of the CT image according to some embodiments of thepresent disclosure;

FIG. 9 is a flowchart illustrating an exemplary process for correctingthe CT image according to some embodiments of the present disclosure;

FIG. 10A and FIG. 10B illustrate exemplary CT images with artifactsaccording to some embodiments of the present disclosure;

FIG. 11A-1 through FIG. 11A-3 and FIG. 11B-1 through FIG. 11B-3illustrate gated PET images of two different respiratory phasesreconstructed overlapped with the same attenuation map withoutcorrection according to some embodiments of the present disclosure;

FIG. 12 illustrate the results of slice-wise respiratory phasedetermination on an attenuation map obtained from an uncorrected 3D CTimage according to some embodiments of the present disclosure; and

FIG. 13A-1 through FIG. 13A-3 and FIG. 13B-1 through FIG. 13B-3illustrate a gated PET image overlapped with an attenuation map withoutcorrection and with a corrected attenuation map, respectively, accordingto some embodiments of the present disclosure.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth by way of examples in order to provide a thorough understanding ofthe relevant disclosure. However, it should be apparent to those skilledin the art that the present disclosure may be practiced without suchdetails. In other instances, well-known methods, procedures, systems,components, and/or circuitry have been described at a relativelyhigh-level, without detail, in order to avoid unnecessarily obscuringaspects of the present disclosure. Various modifications to thedisclosed embodiments will be readily apparent to those skilled in theart, and the general principles defined herein may be applied to otherembodiments and applications without departing from the spirit and scopeof the present disclosure. Thus, the present disclosure is not limitedto the embodiments shown, but to be accorded the widest scope consistentwith the claims.

The terminology used herein is for the purpose of describing particularexample embodiments only and is not intended to be limiting. As usedherein, the singular forms “a,” “an,” and “the” may be intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprise,”“comprises,” and/or “comprising,” “include,” “includes,” and/or“including,” when used in this specification, specify the presence ofstated features, integers, steps, operations, elements, and/orcomponents, but do not preclude the presence or addition of one or moreother features, integers, steps, operations, elements, components,and/or groups thereof.

It will be understood that the term “system,” “engine,” “unit,”“module,” and/or “block” used herein are one method to distinguishdifferent components, elements, parts, section or assembly of differentlevel in ascending order. However, the terms may be displaced by otherexpression if they achieve the same purpose.

Generally, the word “module,” “unit,” or “block,” as used herein, refersto logic embodied in hardware or firmware, or to a collection ofsoftware instructions. A module, a unit, or a block described herein maybe implemented as software and/or hardware and may be stored in any typeof non-transitory computer-readable medium or other storage device. Insome embodiments, a software module/unit/block may be compiled andlinked into an executable program. It will be appreciated that softwaremodules can be callable from other modules/units/blocks or fromthemselves, and/or may be invoked in response to detected events orinterrupts. Software modules/units/blocks configured for execution oncomputing devices may be provided on a computer-readable medium, such asa compact disc, a digital video disc, a flash drive, a magnetic disc, orany other tangible medium, or as a digital download (and can beoriginally stored in a compressed or installable format that needsinstallation, decompression, or decryption prior to execution). Suchsoftware code may be stored, partially or fully, on a storage device ofthe executing computing device, for execution by the computing device.Software instructions may be embedded in a firmware, such as an erasableprogrammable read-only memory (EPROM). It will be further appreciatedthat hardware modules/units/blocks may be included in connected logiccomponents, such as gates and flip-flops, and/or can be included ofprogrammable units, such as programmable gate arrays or processors. Themodules/units/blocks or computing device functionality described hereinmay be implemented as software modules/units/blocks, but may berepresented in hardware or firmware. In general, themodules/units/blocks described herein refer to logicalmodules/units/blocks that may be combined with othermodules/units/blocks or divided into sub-modules/sub-units/sub-blocksdespite their physical organization or storage. The description may beapplicable to a system, an engine, or a portion thereof.

It will be understood that when a unit, engine, module or block isreferred to as being “on,” “connected to,” or “coupled to,” anotherunit, engine, module, or block, it may be directly on, connected orcoupled to, or communicate with the other unit, engine, module, orblock, or an intervening unit, engine, module, or block may be present,unless the context clearly indicates otherwise. As used herein, the term“and/or” includes any and all combinations of one or more of theassociated listed items.

These and other features, and characteristics of the present disclosure,as well as the methods of operation and functions of the relatedelements of structure and the combination of parts and economies ofmanufacture, may become more apparent upon consideration of thefollowing description with reference to the accompanying drawings, allof which form a part of this disclosure. It is to be expresslyunderstood, however, that the drawings are for the purpose ofillustration and description only and are not intended to limit thescope of the present disclosure. It is understood that the drawings arenot to scale.

Provided herein are systems and components for non-invasive imaging,such as for disease diagnosis or research purposes. In some embodiments,the imaging system may be a computed tomography (CT) system, an emissioncomputed tomography (ECT) system, a magnetic resonance imaging (MRI)system, an ultrasonography system, an X-ray photography system, apositron emission tomography (PET) system, or the like, or anycombination thereof.

The following description is provided to help better understandingCT/PET image reconstruction methods and/or systems. The term “image”used in this disclosure may refer to a 2D image, a 3D image, a 4D image,and/or any related image data (e.g., CT data, projection datacorresponding to the CT data). This is not intended to limit the scopethe present disclosure. For persons having ordinary skills in the art, acertain amount of variations, changes, and/or modifications may bededucted under the guidance of the present disclosure. Those variations,changes, and/or modifications do not depart from the scope of thepresent disclosure.

FIG. 1 illustrates an exemplary imaging system according to someembodiments of the present disclosure. An imaging system may produce animage of a subject. As illustrated, the imaging system may include animaging device 110, a controller 120, a data processing system 130, aninput/output device 140, a network 160, and a terminal(s) 170.

In some embodiments, the imaging device 110 may scan a subject, andgenerate a plurality of data relating to the subject. The dataprocessing system 130 may reconstruct an image from the plurality ofdata. In some embodiments, the imaging device 110 may be a medicalimaging device, for example, a PET device, a SPECT device, a CT device,an MRI device, or the like, or any combination thereof (e.g., a PET-CTdevice, a PET-MRI device, or a CT-MRI device). In some embodiments, theimaging device 110 may include a scanner to scan a subject and obtaininformation relating to the subject. In some embodiments, the imagingdevice 110 may be a radioactive scanning device. The radioactivescanning device may include a radioactive scanning source to emitradioactive rays to the subject being scanned. The radioactive rays mayinclude, for example, particle rays, photon rays, or the like, or anycombination thereof. The particle rays may include neutron, proton,electron, μ-meson, heavy ion, or the like, or any combination thereof.The photon rays may include X-ray, γ-ray, α-ray, β-ray, ultraviolet,laser, or the like, or any combination thereof. In some embodiments, thephoton ray may be X-ray, and the imaging device 110 may be a CT system,a digital radiography (DR) system, a multi-modality system, or the like,or any combination thereof. Exemplary multi-modality system may includea computed tomography-positron emission tomography (CT/PET) system, acomputed tomography-magnetic resonance imaging (CT-MRI) system, or thelike.

In some embodiments, the imaging device 110 may be the CT/PET imagingdevice including a gantry 111, a detector 112, a detecting region 113, atable 114, and a radioactive scanning source 115. The gantry 111 maysupport the detector 112 and the radioactive scanning source 115. Asubject may be placed on the table 114 for scanning. The radioactivescanning source 115 may emit radioactive rays to the subject. Thedetector 112 may detect radiation events (e.g., gamma photons) emittedfrom the detecting region 113. In some embodiments, the detector 112 mayinclude one or more detector units. The detector 112 may include ascintillation detector (e.g., a cesium iodide detector), a gas detector,etc. The detector 112 may be and/or include a single-row detector inwhich a plurality of detector units are arranged in a single row and/ora multi-row detector in which a plurality of detector units are arrangedin multiple rows.

The controller 120 may control the imaging device 110, the input/outputdevice 140, and/or the data processing system 130. In some embodiments,the controller 120 may control the X-ray generating unit and/or theX-ray detecting unit (if any) of the imaging device 110. The controller120 may receive information from or send information to the imagingdevice 110, the input/output device 140, and/or the data processingsystem 130. For example, the controller 120 may receive commands fromthe input/output device 140 provided by a user. As another example, thecontroller 130 may process data input by a user via the input/outputunit 140 and transform the data into one or more commands. As a furtherexample, the controller 120 may control the imaging device 110, theinput/output device 140, and/or the data processing system 130 accordingto the received commands or transformed commands. As still a furtherexample, the controller 120 may receive image signals or data related toa subject from the imaging device 110. As still a further example, thecontroller 120 may send image signals or data to the data processingsystem 130. As still a further example, the controller 120 may receiveprocessed data or constructed image from the data processing system 130.As still a further example, the controller 120 may send processed dataor constructed image to the input/output device 140 for displaying. Insome embodiments, the controller 120 may include a computer, a program,an algorithm, a software, a storage device, one or more interfaces, etc.Exemplary interfaces may include the interfaces with the imaging device110, the input/output device 140, the data processing system 130, and/orother modules or units in the imaging system.

In some embodiments, the controller 120 may receive a command providedby a user including, for example, an imaging technician, a doctor, etc.Exemplary commands may relate to a scan time, a location of the subject,the location of a couch on which the subject lies, subjection or arotating speed of the gantry, a specific parameter relating to athreshold that may be used in the image reconstruction process, or thelike, or any combination thereof. In some embodiments, the controller120 may control the data processing system 130 to select differentalgorithms to process the raw data of an image.

The data processing system 130 may process information received from theimaging device 110, the controller 120, the input/output device 140,and/or the terminal 170. In some embodiments, the data processing system130 may generate one or more CT images based on the information. Thedata processing system 130 may deliver the images to the input/outputdevice 140 for display. In some embodiments, the data processing system130 may perform operations including, for example, data preprocessing,image reconstruction, image correction, image composition, lookup tablecreation, or the like, or any combination thereof. In some embodiments,the data processing system 130 may process data based on an algorithmincluding, for example, the Fourier slice theorem, a filtered backprojection algorithm, fan-beam reconstruction, iterative reconstruction,or the like, or any combination thereof. Merely by way of example, imagedata regarding a lung may be processed in the data processing system130. In some embodiments, the data processing system 130 may generate areconstructed PET image based on a CT image. In some embodiments,artifacts may appear in the PET image because of the mismatch of the PETdata and CT data. The data processing system 130 may apply variousalgorithms or techniques to reduce the artifacts. For example, theprojection data relating to the chest of the object may be processed toreduce the artifacts.

For brevity, an image, or a portion thereof (e.g., a region of interest(ROI) in the image) corresponding to an object (e.g., a tissue, anorgan, a tumor, etc., of a subject (e.g., a patient, etc.)) may bereferred to as an image, or a portion of thereof (e.g., an ROI) of orincluding the object, or the object itself. For instance, an ROIcorresponding to the image of a lung or a heart may be described as thatthe ROI includes a lung or a heart. As another example, an image of orincluding a chest may be referred to a chest image, or simply a chest.For brevity, that a portion of an image corresponding to an object isprocessed (e.g., extracted, segmented, etc.) may be described as theobject is processed. For instance, that a portion of an imagecorresponding to a lung is extracted from the rest of the image may bedescribed as that the lung is extracted.

In some embodiments, the data processing system 130 may generate acontrol signal relating to the configuration of the imaging device 110.In some embodiments, the result generated by the data processing system130 may be provided to other modules or units in the system including,e.g., a database (not shown), a terminal (not shown) via the network160. In some embodiments, the data from the data processing system 130may be transmitted to a storage (not shown) for storing.

The input/output device 140 may receive or output information. In someembodiments, the input/output device 140 may include a keyboard, a touchscreen, a mouse, a remote controller, or the like, or any combinationthereof. The input and/or output information may include programs,software, algorithms, data, text, number, images, voices, or the like,or any combination thereof. For example, a user may input some initialparameters or conditions to initiate an imaging process. As anotherexample, some information may be imported from an external resourceincluding, for example, a floppy disk, a hard disk, a wired terminal, awireless terminal, or the like, or any combination thereof. The outputinformation may be transmitted to a display, a printer, a storagedevice, a computing device, or the like, or a combination thereof. Insome embodiments, the input/output device 140 may include a graphicaluser interface. The graphical user interface may facilitate a user toinput parameters, and intervene in the data processing procedure.

The network 160 may include any suitable network that can facilitateexchange of information and/or data for the imaging system 100. In someembodiments, one or more components of the imaging system 100 (e.g., theimaging device 110, the controller 120, the data processing system 130,the input/output device 140, and/or the terminal 170, etc.) maycommunicate information and/or data with one or more other components ofthe imaging system 100 via the network 160. For example, the dataprocessing system 130 may obtain image data from the imaging device 110via the network 160. As another example, the data processing system 130may obtain user instructions from the terminal 170 via the network 160.

The network 160 may be and/or include a public network (e.g., theInternet), a private network (e.g., a local area network (LAN), a widearea network (WAN)), etc.), a wired network (e.g., an Ethernet network),a wireless network (e.g., an 802.11 network, a Wi-Fi network, etc.), acellular network (e.g., a Long Term Evolution (LTE) network), a framerelay network, a virtual private network (“VPN”), a satellite network, atelephone network, routers, hubs, switches, server computers, and/or anycombination thereof. Merely by way of example, the network 160 mayinclude a cable network, a wireline network, a fiber-optic network, atelecommunications network, an intranet, a wireless local area network(WLAN), a metropolitan area network (MAN), a public telephone switchednetwork (PSTN), a Bluetooth™ network, a ZigBee™ network, a near fieldcommunication (NFC) network, or the like, or any combination thereof. Insome embodiments, the network 160 may include one or more network accesspoints. For example, the network 160 may include wired and/or wirelessnetwork access points such as base stations and/or internet exchangepoints through which one or more components of the imaging system 100may be connected to the network 160 to exchange data and/or information.

The terminal(s) 170 may include a mobile device 171, a tablet computer172, a laptop computer 173, or the like, or any combination thereof. Insome embodiments, the mobile device 171 may include a smart home device,a wearable device, a mobile device, a virtual reality device, anaugmented reality device, or the like, or any combination thereof. Insome embodiments, the smart home device may include a smart lightingdevice, a control device of an intelligent electrical apparatus, a smartmonitoring device, a smart television, a smart video camera, aninterphone, or the like, or any combination thereof. In someembodiments, the wearable device may include a bracelet, a footgear,eyeglasses, a helmet, a watch, clothing, a backpack, a smart accessory,or the like, or any combination thereof. In some embodiments, the mobiledevice may include a mobile phone, a personal digital assistant (PDA), agaming device, a navigation device, a point of sale (POS) device, alaptop, a tablet computer, a desktop, or the like, or any combinationthereof. In some embodiments, the virtual reality device and/or theaugmented reality device may include a virtual reality helmet, virtualreality glasses, a virtual reality patch, an augmented reality helmet,augmented reality glasses, an augmented reality patch, or the like, orany combination thereof. For example, the virtual reality device and/orthe augmented reality device may include a Google Glass™, an OculusRift™, a Hololens™, a Gear VR™, etc. In some embodiments, theterminal(s) 170 may be part of the data processing system 130.

The gating system 180 may collect information relating to, for example,breathing, heartbeat, etc. The gating system 180 may analyze theinformation to obtain a motion signal including, for example, arespiration signal, a cardiac motion signal, etc. The gating system mayinclude a gating camera for detecting motion of the subject, a controlpanel, a marker fixed on surface of the subject for indicating motion ofthe subject, or the like, or any combination thereof. In someembodiments, the gating camera may be an infrared camera. For example,when the imaging device 110 is scanning the patient, the gating systemmay be triggered automatically. The gating system may collectinformation associated with respiration motion. The data collected bythe gating system may be stored together with the PET data or CT data.

In some embodiments, the imaging device 110, the controller 120, thedata processing system 130, the input/output device 140, the terminal170, and the gating system 180 may be connected to or communicate witheach other directly. In some embodiments, the imaging device 110, thecontroller 120, the data processing system 130, the input/output device140 may be connected to or communicate with each other via a network160. In some embodiments, the imaging device 110, the controller 120,the data processing system 130, the input/output device 140 may beconnected to or communicate with each other via an intermediate unit(not shown in FIG. 1). The intermediate unit may be a visible componentor an invisible field (radio, optical, sonic, electromagnetic induction,etc.). The connection between different units may be wired or wireless.The wired connection may include using a metal cable, an optical cable,a hybrid cable, an interface, or the like, or any combination thereof.The wireless connection may include using a Local Area Network (LAN), aWide Area Network (WAN), a Bluetooth, a ZigBee, a Near FieldCommunication (NFC), or the like, or any combination thereof. Thenetwork 160 that may be used in connection with the present systemdescribed herein are not exhaustive and are not limiting.

The CT/PET system described herein is merely provided for illustratingan example of the imaging device 110, and not intended to limit thescope of the present application. The CT/PET system may find itsapplications in different fields such as, for example, medicine orindustry. As another example, the imaging device 110 may be used ininternal inspection of components including e.g., flaw detection,security scanning, failure analysis, metrology, assembly analysis, voidanalysis, wall thickness analysis, or the like, or any combinationthereof.

It should be noted that the above description about the imaging systemis merely an example, and should not be understood as the onlyembodiment. To those skilled in the art, after understanding the basicprinciples of the connection between different units, the units andconnection between the units may be modified or varied without departingfrom the principles. The modifications and variations are still withinthe scope of the current application described above. In someembodiments, these units may be independent, and in some embodiments,part of the units may be integrated into one unit to work together.

FIG. 2 is a schematic diagram illustrating exemplary hardware andsoftware components of a computing device 200 on which data processingsystem 130, may be implemented according to some embodiments of thepresent disclosure. For example, the processing module 440 may beimplemented on the computing device 200 and configured to performfunctions of the data processing system 130 described in thisdisclosure.

The computing device 200 may be a general purpose computer or a specialpurpose computer, both may be used to implement an on-demand system forthe present disclosure. The computing device 200 may be used toimplement any component of the on-demand service as described herein.For example, the data processing system 130 may be implemented on thecomputing device 200, via its hardware, software program, firmware, orany combination thereof. Although only one such computer is shown, forconvenience, the computer functions relating to the on-demand service asdescribed herein may be implemented in a distributed fashion on a numberof similar platforms, to distribute the processing load.

The computing device 200, for example, may include COM ports 260connected to and from a network connected thereto to facilitate datacommunications. The computing device 200 may also include a centralprocessing unit (CPU) 230, in the form of one or more processors, forexecuting program instructions. The exemplary computer platform mayinclude an internal communication bus 220, program storage and datastorage of different forms, for example, a disk 210, and a read onlymemory (ROM) 240, or a random access memory (RAM) 250, for various datafiles to be processed and/or transmitted by the computer. The exemplarycomputer platform may also include program instructions stored in theROM 240, RAM 250, and/or other type of non-transitory storage medium tobe executed by the CPU 230. The methods and/or processes of the presentdisclosure may be implemented as the program instructions. The computingdevice 200 also includes an I/O component 270, supporting input/outputbetween the computer and other components therein such as user interfaceelements 280. The computing device 200 may also receive programming anddata via network communications.

Merely for illustration, only one CPU and/or processor is described inthe computing device 200. However, it should be noted that the computingdevice 200 in the present disclosure may also include multiple CPUsand/or processors, thus operations and/or method steps that areperformed by one CPU and/or processor as described in the presentdisclosure may also be jointly or separately performed by the multipleCPUs and/or processors. For example, if in the present disclosure theCPU and/or processor of the computing device 200 executes both operationA and operation B, it should be understood that operation A andoperation B may also be performed by two different CPUs and/orprocessors jointly or separately in the computing device 200 (e.g., thefirst processor executes operation A and the second processor executesoperation B, or the first and second processors jointly executeoperations A and B).

FIG. 3 is a schematic diagram illustrating exemplary hardware and/orsoftware components of an exemplary mobile device 300 on which a userterminal may be implemented according to some embodiments of the presentdisclosure. As illustrated in FIG. 3, the mobile device 300 may includea communication platform 310, a display 320, a graphics processing unit(GPU) 330, a central processing unit (CPU) 340, an I/O 350, a memory360, an operation system (OS) 370, applications 380, and a storage 390.In some embodiments, any other suitable component, including but notlimited to a system bus or a controller (not shown), may also beincluded in the mobile device 300. In some embodiments, a mobileoperating system 370 (e.g., iOS™, Android™, Windows Phone™, etc.) andone or more applications 380 may be loaded into the memory 360 from thestorage 390 in order to be executed by the CPU 340. The applications 380may include a browser or any other suitable mobile apps for receivingand rendering information relating to image processing or otherinformation from the data processing system 130. User interactions withthe information stream may be achieved via the I/O 350 and provided tothe data processing system 130 and/or other components of the imagingsystem 100 via the network 160.

FIG. 4 is a block diagram illustrating an exemplary data processingsystem 130 according to some embodiments of the present disclosure. Asshown in FIG. 4, the data processing system 130 may include a dataacquisition module 410, a storage module 420, a display module 430, anda processing module 440. At least a portion of the data processingsystem 130 may be implemented on a computing device as illustrated inFIG. 2, or a mobile device as illustrated in FIG. 3.

The data acquisition module 410 may acquire data. The data acquired maybe generated from the imaging device 110, or the controller 120. In someembodiments, the data may be acquired from an external data source viathe network 160. The data acquired may be 3D image data, and/or 2D imagedata. The data acquired may include information regarding a whole humanbody, a lung, a bronchus, a thorax, or the like, or any combinationthereof. In some embodiments, the data acquisition module 410 mayinclude a wireless receiver to receive data via the network 160.

The storage module 420 may store data. The data stored may be anumerical value, a signal, an image, information of a subject, aninstruction, an algorithm, or the like, or a combination thereof. Thedata stored may be acquired by the data acquisition module 410, importedvia the input/output device 140, generated in the processing module 440,or pre-stored in the storage module 420 during system initialization orbefore an operation of data processing. The storage module 420 mayinclude a system storage (e.g., a disk) that is provided integrally(i.e. substantially non-removable), or a storage that is removablyconnectable to the system via, for example, a port (e.g., a UBS port, afirewire port, etc.), a drive (a disk drive, etc.), etc. The storagemodule 420 may include, for example, a hard disk, a floppy disk,selectron storage, random access memory (RAM), dynamic random accessmemory (DRAM), static random access memory (SRAM), bubble memory, thinfilm memory, magnetic plated wire memory, phase change memory, flashmemory, a cloud disk, or the like, or a combination thereof. The storagemodule 420 may be connected to or communicate with one or more of thedata acquisition module 410, the processing module 440, and the displaymodule 430. In some embodiments, the storage module 420 may beoperationally connected with one or more virtual storage resources(e.g., cloud storage, a virtual private network, other virtual storageresources, etc.) via the network 160.

The display module 430 may display information. The informationdisplayed may include a value, a text, an image, and information of asubject. The information displayed may be transmitted from the dataacquisition module 410, the storage module 420, and/or the processingmodule 440. In some embodiments, the display module 430 may transforminformation to the input/output device 140 for display. In someembodiments, the display module 430 may transform the image data that isgenerated from the processing module 440 for display. In someembodiments, the display module 430 may transform the image datadirectly extracted from the storage module 420 or the network 160 fordisplay.

The processing module 440 may process data and construct an image. Thedata may be acquired from the data acquisition module 410, the storagemodule 420, etc. The image constructed may be transmitted by theprocessing module 440 to the display module 430. In some embodiments,the data processed may be acquired from an external data source via thenetwork 160. In some embodiments, the processing module 440 mayreconstruct image data to generate one or more images. The image datamay be reconstructed by using a reconstruction algorithm. Thereconstruction algorithm may be an analytic reconstruction algorithm, aniterative reconstruction algorithm, or based on compressed sensing (CS).In some embodiments, the processing module 440 may segment the imagedata to obtain an image of a specific portion of a subject, for example,a heart, a blood vessel, a lung, a bronchus, or the like, or anycombination thereof.

In some embodiments, the processing module 440 may include a universalprocessor, e.g., a programmable logic device (PLD), anapplication-specific integrated circuit (ASIC), a microprocessor, asystem on chip (SoC), a digital signal processor (DSP), or the like, orany combination thereof. Two or more of these universal processors inthe processing module 440 may be integrated into a hardware device, ortwo or more hardware devices independently with each other. It should beunderstood, the universal processor in the processing module 440 may beimplemented via various methods. For example, in some embodiments, theprocessing procedure of the processing module 440 may be implemented byhardware, software, or a combination of hardware software, not only by ahardware circuit in a programmable hardware device in an ultra largescale integrated circuit, a gate array chip, a semiconductor such atransistor, or a field programmable gate array, a programmable logicdevice, and also by a software performed by various processors, and alsoby a combination of the hardware and the software above (e.g.,firmware).

It should be noted that the above description about the data processingsystem 130 is merely an example, and should not be understood as theonly embodiment. To those skilled in the art, after understanding thebasic principles of the connection between different units, the unitsand connection between the units may be modified or varied withoutdeparting from the principles. The modifications and variations arestill within the scope of the current application described above. Forexample, the display module 430 may be unnecessary before imagedisplaying in the input/output device 140.

FIG. 5 is a block diagram illustrating an exemplary processing module440 according to some embodiments of the present disclosure. As shown inFIG. 5, the processing module 440 may include a gating unit 510, amotion phase determination unit 530, a motion vector field determinationunit 520, a reconstruction unit 550, a motion deformation processingunit 540, and a registration unit 560. In some embodiments, theprocessing module 440 may be implemented by CPU 230 in the computingdevice 200, CPU 340 in the mobile device 300, or any component in theimaging system 100. At least a portion of the processing module 440 maybe implemented on a computing device as illustrated in FIG. 2 or amobile device as illustrated in FIG. 3. A module may be a hardwarecircuit that is designed to perform one or more of the followingactions, a set of instructions stored in one or more storage media,and/or any combination of the hardware circuit and the one or morestorage media.

The gating unit 510 may gate PET data into a plurality of groups ofgated PET data. The PET data may be the projection data of a PETscanning. A group of gated PET data may be used to reconstruct aplurality of gated PET images. The gating of the PET data may beperformed by dividing the PET data into a plurality of groups or framesbased on a time interval associated with a motion. The time interval maybe determined based on the amplitudes of the motion, and/or thevariation of the amplitudes with time.

For example, in a respiratory cycle, from an end-expiration to anend-inspiration, the motion amplitude may increase from a lowest valueto a highest value. An average value of the lowest value and the highestvalue may be determined to be a midway amplitude. In this case, a firsttime interval may be determined to be the time period between the timepoint corresponding to an end-expiration and the time pointcorresponding to the midway amplitude that first appears during therespiration motion after the end-expiration. A second time interval maybe determined to be the time period between the time point correspondingto the timing of the midway amplitude and the time point correspondingto the end-inspiration that first appears during the respiration motionafter the midway amplitude. Similarly, the number of groups may vary ifthere are more midway amplitudes. In some embodiments, the time intervalmay be determined based on a predetermined value. For example, thepredetermined value may be a constant. The PET data may be transmittedor stored in an electronic data form. The electronic data form mayinclude a digital imaging and communications in medicine (DICOM) dataform, a Mosaic data form, an Analyze data form, a neuroimaginginformatics technology initiative (NIfTI) data form, or the like, or anycombination thereof. For example, the PET data may be stored in thestorage module 420 in the DICOM data form.

The PET images and/or the PET data may be obtained from the acquisitionmodule 410, or any other components in the imaging system 100. Forexample, the PET data may be generated by scanning a thorax of a patientusing the imaging system 100 (e.g., a PET imaging system). In someembodiments, the PET data may be transmitted or received in the form ofan electronic signal. The electronic signal may be used to encode thePET data. Merely by way of example, the PET data may be obtained from acloud storage (e.g., a public cloud) via the network 160. In someembodiments, the PET data may be reconstructed to a plurality of PETimages.

In some embodiments, the PET images and/or the PET data may correspondto CT data, or at least one CT image. For instance, the PET imagesand/or the PET data, and the CT data and/or CT image(s) may be obtainedby scanning a same area of a same subject (for example, a patient). TheCT data may be obtained by scanning a patient before or after a PETscanning of the patient. For example, the CT data may be acquired byscanning a patient who remains still. After the CT scanning, the PETdata may be acquired by scanning the patient at (essentially) the samepatient position. As another example, the CT image or CT data may beobtained after a PET scanning of the patient.

In some embodiments, the PET data and/or the corresponding CT data maybe processed. For example, the PET data and/or the corresponding CT datamay be used to reconstruct a plurality of PET images and/or a pluralityof CT images. The plurality of CT images may be CT images in atransverse plane, a coronal plane, or a sagittal plane. The transverseplane, the coronal plane, and the sagittal plane are used in the medicalfield and are perpendicular to each other. For example, the CT imagesmay include a plurality of 2D images in a transverse plane. In someembodiments, the corresponding CT data may be processed for anattenuation correction of a corresponding PET image reconstruction.Description regarding attenuation correction of a PET imagereconstruction may be found elsewhere in the present disclosure. See,for example, FIG. 6A and the description thereof.

The PET data may be gated or divided based on a gating condition. Forinstance, according to the gating condition, the PET data may be dividedinto a plurality of groups of gated PET data. In some embodiments, thegating condition may be associated with a type of motion of the subject(or referred to as a subject motion). The subject motion may include arespiratory motion (or referred to as a respiration motion) with aplurality of respiratory phases (related description may be foundelsewhere in the present disclosure), a cardiac motion with a pluralityof cardiac phases, a gastrointestinal motion with a plurality ofgastrointestinal phases, a skeletal muscle motion with a plurality ofskeletal muscle motion phases, or the like, or any combination thereof.For example, the subject (e.g., a patient) may undergo respiratorymotion during a PET scanning and/or a CT scanning. The methods andsystems are described with reference to a respiratory motion forillustrated purposes, and not intended to limit the scope of the presentdisclosure. The systems and methods disclosed herein may be applied inthe context of other motion types including, for example, cardiacmotion, gastrointestinal motion, skeletal muscle motion, etc., or acombination thereof.

The gating condition may include a gating parameter, a time interval, aregion of interest, a compression algorithm, or the like, or anycombination thereof. The gating parameter may include a respiratoryphase, a cardiac phase, a gastrointestinal phase, a skeletal musclemotion phase, or the like, or any combination thereof. The respiratoryphase may correspond to respiratory motion of the subject (e.g., thepatient). The respiratory motion of the subject may include an inhalingphase (or referred to as an inspiratory phase) and/or an exhaling phase(or referred to as an expiratory phase). For example, in the inhalingphase, the patient may expand his/her chest to cause a negative pressurein the chest. The negative pressure may cause the air to flow into thelungs of the patient. As another example, in the exhaling phase, thepatient may shrink the chest to cause a positive pressure in the chest.The positive pressure may press the air out of the lungs.

In some embodiments, the gating unit 510 may divide the PET data basedon the motion information acquired using the gating system 180. Thegating system may include a device for detecting a motion of thesubject, a control panel, a marker fixed on surface of the subject forindicating motion of the subject, or the like, or any combinationthereof. In some embodiments, the gating system may include a motiondetection device such as, for example, a gating camera, a belt securedaround the chest of the subject or another pressure measurementtechnique or device to measure the change of pressure during thebreathing of the subject. In some embodiments, the gating camera may bean infrared camera. The gating system 180 may be used to collectinformation relating to, for example, respiration, heartbeat, etc. Thegating system 180 may analyze the information to obtain the gatingparameter (e.g., the respiratory phase). In some embodiments, motioninformation may be derived from the imaging data including, for example,PET data. Exemplary gating techniques, including self-gating, may befound in, for example, U.S. application Ser. No. 15/386,048 filed Dec.21, 2016 and Ser. No. 15/618,425 filed Jun. 9, 2017, both entitled“METHODS AND SYSTEMS FOR EMISSION COMPUTED TOMOGRAPHY IMAGERECONSTRUCTION,” the contents of which are hereby incorporated byreference.

In some embodiments, the gated PET image may be the PET image associatedwith a gating parameter (e.g., a respiratory phase). For example, thegated PET image may include a first PET image corresponding to a firstgroup of PET data and a second PET image corresponding to a second groupof PET data. The first group of PET data may correspond to a firstrespiratory phase (e.g., the inhaling phase of the patient), and thesecond group of PET data may correspond to a second respiratory phase(e.g., the exhaling phase of the patient). A first gated PET imagereconstructed using the first group of PET data may correspond to thefirst respiratory phase. A second gated PET image reconstructed usingthe second group of PET data may correspond to the second respiratoryphase. The gated PET images may be gated 3D PET images. More details ofthe operation of gating the PET data may be found elsewhere in thepresent disclosure. See, for example, description regarding operation630 in FIG. 6A or process 700 in FIG. 7. The gated PET image may befurther processed in the motion vector field determination unit 520.

The motion vector field determination unit 520 may determine a motionvector field based on the gated PET images. In some embodiments, themotion vector field may include a plurality of motion vectors. A motionvector may be used to describe the motion of a spatial point of thesubject between the motion phases corresponding to the two gated PETimages. In some embodiments, a motion vector may be determined byregistering the two gated PET images. For example, after registering thetwo gated PET images, locations of two voxels in the gated PET imagescorresponding to a same spatial point of the subject may be determined.Then the motion vector field determination unit 520 may determine thecorresponding motion vector of the spatial point based on the locationsof the corresponding two voxels. A motion vector field may be a setincluding a portion or all of the motion vectors between two gated PETimages. The motion vector field may be used to describe a motionrelationship of spatial points between two motion phases correspondingto the two gated PET images.

In some embodiments, a motion vector field may be determined based ontwo gated PET images corresponding to two different respiratory phases(e.g., a first respiratory phase, and a second respiratory phasedifferent from the first respiratory phase). In some embodiments, amotion vector field may be determined based on two sequential gated PETimages that correspond to two sequential respiratory phases among therespiratory phases whose PET data are acquired. For example, the motionvector field determination unit 520 may determine a motion vector fieldcorresponding to an Nth gated PET image and an (N+1)th gated PET image.

In some embodiments, the motion vector field determination unit 520 maydetermine a motion vector field based on the registration of each of thegated PET images with a reference image. The reference image may bedetermined by selecting one gated PET image from the plurality of gatedPET images based on a selection condition (e.g., a sequence of theimage). The gated PET images may be 3D PET images. For example, a firstgated PET image may be selected as the reference image. The other gatedPET images may be registered with the first gated PET image. Theregistration may be performed by the registration unit 560.

The motion phase determination unit 530 may determine a similaritybetween a CT image and a plurality of gated PET images to determine therespiratory phase of the CT image.

The similarities between a CT image and the gated PET images may be usedto determine whether the CT image is the same as or similar to one ofgated PET images in terms of a motion phase. Exemplary operations forassessing the similarity of a CT image and a gated PET image may befound elsewhere in the present disclosure. See, for example, 660 inFIGS. 6A and 810 in FIG. 8, and the description thereof. In someembodiments, the similarity may include a pixel-based similarity, avoxel-based similarity, an entropy-based similarity, a mutualinformation similarity, or the like, or any combination thereof. Thepixel-based similarity may be determined by comparing at least one ofpixels between the CT image and the PET image. The voxel-basedsimilarity may be determined by comparing at least one of voxels betweenthe CT image (e.g., a 3D CT image) and the PET image (e.g., a 3D PETimage). The entropy-based similarity may be determined by comparinginformation gain between the CT image and the PET image. The mutualinformation similarity may be determined by comparing mutual informationbetween the CT image and the PET image.

The similarity may be presented as a number, a percentage, a value, atext, a matrix, a determinant, or the like, or any combination thereof.For example, the similarity may be expressed as 2, 4, 5, 30, or anyinteger. In some embodiments, one or more similarities may be ranked.For example, the one or more similarities may be ranked from the minimumto the maximum. As another example, the motion phase determination unit530 may analyze the similarities of a CT image with respect to theplurality of gated PET images, and identify the maximum similarity basedon the analysis. The CT image and the gated PET image with the maximumsimilarity with the CT image may be considered corresponding to the sameor the most similar motion phase with the CT image.

In some embodiments, for a CT image, the motion phase determination unit530 may further determine a motion phase of the CT image based on thesimilarities between the CT image and the gated PET images. The motionphase of the CT image may correspond to the subject motion such as therespiratory motion with a plurality of respiratory phases, the cardiacmotion with a plurality of cardiac phases, the gastrointestinal motionwith a plurality of gastrointestinal phases, a skeletal muscle motionwith a plurality of skeletal muscle motion phases, etc. For example, thephase of the CT image may be the respiratory phase of the CT image. TheCT image may be determined to correspond to the motion phase (e.g., therespiratory phase) of the gated PET image with the maximum similarityamong the gated PET images. For each of the plurality of CT images, therespective motion phase of the CT image may be so determined.

The motion deformation processing unit 540 may deform a CT image. Themotion deformation processing unit 540 may process the CT image bydetermining a deformation vector field based on the similarities and thecorresponding motion vector fields with respect to the plurality ofgated PET images. For example, the deformation processing unit 540 maydetermine a gated PET image achieving the maximum similarity with the CTimage, and determining the motion vector field of the gated PET imagewith respect to the reference image as the corresponding motion vectorfield. In some embodiments, the motion deformation processing unit 540may process the CT image based on the deformation vector to generate acorrected CT image. For example, the motion deformation processing unit540 may deform the CT image with a first respiratory phase of the PETimage into a corrected CT image with a second respiratory phase.Description regarding the deformation of a CT image to generate acorrected CT image may be found elsewhere in the present disclosure.See, for example, 680 in FIG. 6A or the process 900 in FIG. 9, and thedescription thereof.

The reconstruction unit 550 may reconstruct a PET image based on thecorrected CT images and the PET data. In some embodiments, the PET imagemay integrate information of the PET data and the corrected CT images.In some embodiments, the anatomical information of the subject may beobtained from the corrected CT images. In some embodiments, thereconstructed PET image may exhibit functional information such as ametabolic intensity of a tumor, etc. The functional information may beobtained from the PET data. For example, the reconstructed PET image mayshow a tumor with a high metabolic intensity located in a liver region,while a corrected CT image corresponding to the reconstructed PET imagemay show a more precise location or contour of the tumor.

The corrected CT images may be used to generate an attenuation mapincluding a plurality of attenuation coefficients by the reconstructionunit 550. The attenuation map may be used to correct the PET data. ThePET data and the attenuation map may be used to reconstruct a PET imageby the reconstruction unit 550.

The registration unit 560 may register the gated PET images. In someembodiments, the registration unit 560 may register the gated PET imageswith a reference PET image. The reference PET image may be one of thegated 3D PET images. Any one of the gated 3D PET images may bedesignated as the reference PET image, or referred to as the referencegated 3D PET image. The registration may be implemented based on atleast one registration algorithm. Exemplary registration algorithms mayinclude a point-based registration algorithm (e.g., ananatomic-landmark-based registration algorithm), a curve-basedregistration algorithm, a surface-based registration algorithm (e.g., ansurface-profile-based surface profile), a spatial alignment registrationalgorithm, a cross-correlation registration algorithm, amutual-information-based registration algorithm, a sequential similaritydetection algorithm (SSDA), a nonlinear transformation registrationalgorithm, an optical flow, demons registration algorithm, B-splineregistration algorithm, or the like, or any combination thereof.

In some embodiments, the registration may be performed based on rigidtransformation, an affine transformation, a projection transformation, anonlinear transformation, an optical-flow-based registration, asimilarity measurement, or the like, or any combination thereof. Thesimilarity measurement may include a mutual-information-basedmeasurement, a Fourier-analysis-based measurement, or the like, or anycombination thereof. Merely by way of example, the registration unit 560may use the optical flow algorithm to register the gated PET images, andgenerate motion vector fields for the plurality of gated PET images.

The gating unit 510, the motion vector field determination unit 520, themotion phase determination unit 530, the motion deformation processingunit 540, the reconstruction unit 550, and the registration unit 560 inthe processing module 440 may be connected to or communicate with eachother via a wired connection or a wireless connection. The wiredconnection may include a metal cable, an optical cable, a hybrid cable,or the like, or any combination thereof. The wireless connection mayinclude a Local Area Network (LAN), a Wide Area Network (WAN), aBluetooth, a ZigBee, a Near Field Communication (NFC), or the like, orany combination thereof. In some embodiments, any two of the modules maybe combined as a single module, and any one of the modules may bedivided into two or more units.

FIG. 6A is a flowchart illustrating an exemplary process 600 forreconstructing a PET image according to some embodiments of the presentdisclosure. At least a portion of the process 600 may be implemented ona computing device as illustrated in FIG. 2 or a mobile device asillustrated in FIG. 3. In some embodiments, one or more operations ofprocess 600 for reconstructing a PET image may be implemented in theimage processing system 100 illustrated in FIG. 1. For example, theprocess 600 may be stored in the storage module 420 of the dataprocessing system 130 in the form of instructions, and invoked and/orexecuted by the data processing system 130 (e.g., the processor 230 ofthe data processing system 130).

In 610, the acquisition module 410 may obtain a plurality of CT imagescorresponding to a scanning area of a subject. The plurality of CTimages may be 2D images, and may also refer to CT image layers (e.g.,slice images) of a 3D CT image. The CT image layers may correspond to aportion of the 3D CT image. For instance, the 3D CT image includes tenCT image layers, and some or all of the ten CT image layers areprocessed according to process 600. In some embodiments, the pluralityof CT images may also include processed CT images (e.g., one or moreattenuation maps relating to the CT images). The plurality of CT imagesmay be generated by a CT scanner, or transmitted from a storage devicevia the network 160. The scanning area may include a brain, a lung, aliver, a kidney, a bone, any organ or region of interest (ROI)associated with a subject (e.g., a patient). In some embodiments, thescanning area may include a whole body. The subject (e.g., a patient)may need to keep still or maintain his/her position during the scanning.For example, if the scanning area is the spine of the patient, thepatient may lie on the abdomen on the table 114 and keep the samepatient position for a few minutes (e.g., about 3 minutes, about 5minutes, about 10 minutes, about 15 minutes, etc.), and during thescanning the patient may breathe normally.

In some embodiments, the subject may include a patient, an animal, aphantom, or a portion thereof including, for example, an artificiallimb, an artificial heart, a tumor, any structure or organ that may beexamined using X rays, or the like, or any combination thereof.

In the CT/PET system, the imaging device 110 may scan a patient. Theprojection scanning may use a ⁶⁸Ge generator to emit ⁶⁸Ge ray. In someembodiments, the projection scanning may use X rays to scan the patient,and obtain the plurality of CT images. The plurality of CT images may beused to correct PET images corresponding to the same Region of Interest(ROI) as the CT images. The CT images used to correct PET images mayhave a high image resolution, e.g., an image resolution of 0.5 mm.

In 620, the acquisition module 410 may also obtain PET datacorresponding to the same scanning area of the subject. The PET data maycorrespond to the scanning area of the CT scanning of the subjectdescribed in connection with 610. For example, if a CT scanning of achest of the patient is performed, the PET scanning of the chest of thepatient may be performed when the patient keeps essentially the samepatient position, in order to facilitate the combination of informationof the PET data and the CT data together. The patient may breathenormally during the PET scanning. In some embodiments, the PET data maycorrespond to different respiratory phases. For example, a first frameof PET data may correspond to one respiratory phase (e.g., the inhalingphase), and a second frame of PET data may correspond to anotherrespiratory phase (e.g., the exhaling phase).

In some embodiments, the speed of CT scanning may be about 1.5seconds/table position. The speed of the PET scanning may be about 5minutes/table position. If the patient undergoes respiratory motion, theacquired CT images or PET images may include motion artifacts. Therespiratory motion of the subject may include an inhaling phase and/oran exhaling phase. In some embodiments, the respiratory motion of thepatient may lead to that a shift (or referred to as mismatch) of thelocation of a same portion (e.g., an organ, a tissue, etc.) of thesubject in a (2D or 3D) CT image with respect to a corresponding gated(2D or 3D) PET image. A 2D CT image may also be referred to as a CTimage layer. A 2D PET image may also be referred to as a PET imagelayer. For example, the location of the liver may be different in the 3DCT image than that in the gated PET image. For example, a plane of thelung (e.g., Oxy or a transverse plane, etc.) may correspond to one 2D CTimage layer and one gated 2D PET image layer, the 2D CT image layer maybe generated corresponding at an inhaling phase of the respiratorymotion, while the gated 2D PET image layer may be obtained during anexhaling phase of the respiratory motion, and the outer edge of a lungin the plane Oxy may show a shift or mismatch between the CT image andthe gated PET image layer. For instance, such a shift or mismatch may beobserved by comparing a PET image layer in coronal or sagittal plane ofa 3D PET image reconstructed from on gated PET data with that of a 3D CTimage.

Merely by way of example, a 3D CT image is used in the reconstruction ofa gated PET image. A point of a lung of the subject in the 3D CT imagedoes not match the same point of lung in the gated PET image due to theshift. The 3D PET image reconstructed directly based on the 3D CT imagemay show low image quality, if the attenuation map generated based onthe 3D CT image is used for reconstructing the 3D PET image.

In 630, the gating unit 510 may gate the PET data with respect todifferent respiratory phases of the subject. The gated PET data may beused to reconstruct one or more gated PET images. The reconstruction maybe performed by the reconstruction unit 550 described elsewhere in thepresent disclosure. The gated PET data may include informationassociated with a respiratory signal of the respiratory motion. In someembodiments, the gating unit 510 may obtain information of a respiratorysignal relating to a respiratory motion from the PET data, and determinethe respiratory signal of the respiratory motion.

In some embodiments, the respiration signal may be a signal acquiredfrom a source other than the PET data. For instance, the external signalmay be determined from the gating system 180. The gating system 180 maycollect information relating to, for example, breathing, heartbeat, etc.The gating system 180 may analyze the information to obtain therespiration signal. The gating system 180 may include a device fordetecting motion of the subject, a control panel, a marker fixed onsurface of the subject for indicating motion of the subject, or thelike, or any combination thereof. In some embodiments, the gating systemmay include a motion detection device, such as, for example, a gatingcamera, or a belt secured around the chest of the subject or anotherpressure measurement technique or device to measure the change ofpressure during the breathing of the subject. In some embodiments, thegating camera may be an infrared camera. For example, if the imagingdevice 110 is scanning the patient, the gating system may be triggeredautomatically. The gating system may collect information associated withrespiration motion. The data collected by the gating system may bestored together with the PET data or CT data.

In some embodiments, the respiratory signal may be approximated by asine function, a cosine function, a polynomial function, a pulsefunction, or the like, or any combination thereof. In some embodiments,the respiratory signal may be expressed in a two-dimensional coordinate.The two-dimensional coordinate may include a first coordinate axis (orthe X axis) representing time, and a second coordinate axis (or the Yaxis) representing amplitude or value. For example, the respirationsignal may be approximated by a sine function in the two-dimensionalcoordinate. The respiration signal may show the amplitude in the Y axis,and the amplitude may vary depending on the time in the X axis. In someembodiments, the respiration signal may be approximated by the sinesignal or the cosine signal. The gating unit 510 may approximate therespiration signal using, for example, the sine function, the cosinefunction, etc. For example, the respiration signal may be approximatedby the formula (1):Y=c*sin(aX+b),  (1)where Y is the amplitude of the respiratory motion, X is the time of therespiratory motion, and a, b, and c are constant parameters.

In some embodiments, the respiratory signal may be divided into aplurality of respiratory phases. In some embodiments, the respiratorysignal may be determined or divided into N respiratory phases, where Nmay be an integer greater than 1. For example, the gating unit 510 maydivide the respiratory signal into 4 respiratory phases, each of whichmay correspond to a different part in a cycle of the respiratory signal.In some embodiments, the gating unit 510 may divide the respiratorysignal into a fixed number of the respiratory phases automatically. Insome embodiments, the gating unit 510 may divide the respiratory phaseinto N respiratory phases according to the instruction of a user (e.g.,a doctor). For example, a user (e.g., a doctor or a radiologictechnologist) may divide the respiratory signal into 3 respiratoryphases based on his/her clinical experiences. The user may providehis/her instruction via a user interface implemented on, e.g., a mobiledevice as illustrated in FIG. 3.

In some embodiments, the respiratory signal may be divided according toan amplitude of the respiratory signal. For example, a cycle of therespiratory signal may be divided based on the amplitude of therespiratory signal. If the amplitude of the respiratory signal issegmented to n parts (e.g., from the maximum amplitude to the minimumamplitude), the n parts of the respiratory signal may correspond to nrespiratory phases. In some embodiments, the respiratory signal may bedivided, based on the time of the respiratory signal, into N parts, andthe N parts may correspond to N respiratory phases. For example, if acycle of the respiratory signal ranges from 0 second to 5 seconds, acycle of the respiratory signal may be divided according to a timeinterval (e.g., 0.5 seconds, or 1 seconds), and this cycle of therespiratory signal may be divided into N respiratory phases (e.g., 5/0.5or 10 respiratory phases, 5/1 or 5 respiratory phases). Exemplary gatingtechniques, including self-gating, may be found in, for example, U.S.application Ser. No. 15/386,048 filed Dec. 21, 2016 and Ser. No.15/618,425 filed Jun. 9, 2017, both entitled “METHODS AND SYSTEMS FOREMISSION COMPUTED TOMOGRAPHY IMAGE RECONSTRUCTION,” the contents ofwhich are hereby incorporated by reference.

In some embodiments, the gating unit 510 may determine a plurality ofgated PET images based on the respiratory phases. In some embodiments,the gated PET images may be reconstructed based on the gated PET data.In some embodiments, the gated PET images may be determined based on therespiratory phases. For example, if the respiratory signal is dividedinto 4 respiratory phases, the gating unit 510 may determine 4 groups ofgated PET data, and the 4 gated PET images may be reconstructed based onthe 4 groups of gated PET data. In some embodiments, a series of gatedPET images may be reconstructed based on a group of gated PET data. Forexample, if a cycle of respiratory motion is divided into 4 respiratoryphases corresponding to 4 time intervals, the PET data that may includedata corresponding to multiple respiratory cycles may be gated toprovide several groups (or referred to as frames) of gated PET datacorresponding to the 4 time intervals; a series of gated PET images(e.g., 4 gated PET images) may be obtained by way of reconstruction ofgroups (or frames) of gated PET data.

In 640, the registration unit 560 may register the gated PET images. In640, the registration unit 560 may register the gated PET images basedon at least one registration algorithm. Exemplary registrationalgorithms may include a point-based registration algorithm (e.g., ananatomic-landmark-based registration algorithm), a curve-basedregistration algorithm, a surface-based registration algorithm (e.g., ansurface-profile-based surface profile), a spatial alignment registrationalgorithm, a cross-correlation registration algorithm, amutual-information-based registration algorithm, a sequential similaritydetection algorithm (SSDA), a nonlinear transformation registrationalgorithm, an optical flow, or the like, or any combination thereof. Insome embodiments, the registration executed in 640 may include anautomatic registration, a semi-automatic registration, or an artificialregistration. In some embodiments, the registration may be performedbased on rigid transformation, an affine transformation, a projectiontransformation, a nonlinear transformation, an optical-flow-basedregistration, a similarity measurement, or the like, or any combinationthereof.

Merely by way of example, the optical flow may include a sparse opticalflow and a dense optical flow. The sparse optical flow may focus on asparse point in a PET image. For instance, a corner point may be used asa sparse point. In some embodiments, the dense optical flow may focus onthe offset of pixels or voxels in the PET image. In some embodiments,the optical flow may further include a Lucas-Kanade algorithm, aHorn-Schunck algorithm, a Buxton-Buxton algorithm, a general variationalalgorithm, a block-based algorithm, a discrete optimization algorithm,or the like, or any combination thereof.

In some embodiments, the registration unit 560 may register each of thegated PET images against a same reference image. The reference image maybe selected from the plurality of gated PET images. For example, thefirst gated PET image may be designated as the reference image, and eachof the gated PET images may be registered with the first gated PETimage. As another example, the last gated PET images may be designatedas the reference images, and each of the gated PET images may beregistered with the last gated PET image.

In 650, the motion vector field determination unit 520 may determine amotion vector field based on the registration. The motion vector fieldmay include a plurality of motion vectors. A motion vector may be usedto describe a motion of a spatial point of the subject between themotion phases corresponding to the two gated PET images. For example, in640 the motion vector field determination unit 520 may determine a firstlocation of a point in the Nth PET image to be (X1, Y1, Z1), and asecond location of the point in the (N+1)th PET image to be (X2,Y2, Z2).The motion vector field determination unit 520 may further determine amotion vector to be (Ux, Uy, Uz) based on the first location and thesecond location of the point, where Ux may be equal to (X1−X2), Uy maybe equal to (Y1−Y2), and Uz may be equal to (Z1−Z2).

In some embodiments, a spatial point corresponding to differentrespiratory phases may be represented as (x, y, z, t), where the x, y,and z represent values in the X axis, the Y axis, and the Z axis,respectively, and the t represents the respiratory phase for a PET imagehaving a voxel corresponding to the spatial point. In some embodiments,the motion vector at the spatial point of (x,y,z) in a respiratory phaseof t may be represented as a coordinate(m_(u)(x,y,z,t),m_(v)(x,y,z,t),m_(w)(x,y,z,t)), where m_(u) represents amotion vector component in the x axis direction, m_(v) represents amotion vector component in the y axis direction, m_(w) represents amotion vector component in the z axis direction. For example, if amotion vector corresponds to a spatial point (3,5,7) and a respiratoryphase numbered as 1, and a reference respiratory phase numbered as 0,the motion vector may be expressed as(m_(u)(3,5,7,1),m_(v)(3,5,6,1),m_(w)(3,5,7,1)), representing a movementof the spatial point (3,5,7) from the reference respiratory phase 0 tothe respiratory phase 1.

In 660, the motion phase determination unit 530 may determine therespiratory phase of the CT image (also referred to as a CT image layerof a 3D CT image). In some embodiments, the determination may be basedon a relationship between the gated PET images and the CT image. A gated3D PET image may include a plurality of gated PET image layerscorresponding to a same respiratory phase of a respiratory motion of asubject. A CT image layer of the 3D CT image and a gated PET image layermay correspond to a same group of spatial points relating to thesubject. A group of spatial points relating to the subject may includespatial points within or on the surface of the subject, or in thevicinity of the subject when the subject is scanned to provide CT imagedata (corresponding to the 3D CT image, the CT image layers, etc.), orPET image data (corresponding to the 3D PET images, the gated 3D PETimages, the gated PET image layers of a gated 3D PET image, etc.) thatare analyzed or processed as described herein.

The relationship between the gated PET images and the CT image layer maybe assessed in terms of the similarities between a plurality of gatedPET image layers and the CT image layer corresponding to a same group ofspatial points relating to the subject. The plurality of gated PET imagelayers, one from each of a plurality of gated 3D PET images, maycorrespond to different respiratory phases of a respiratory motion ofthe subject during an image scan. In some embodiments, the similaritybetween the CT image layer and a gated PET image layer may bedetermined. For example, one CT image layer may be compared with threegated PET image layers (e.g., a first gated PET image layer, a secondgated PET image layer, and a third gated PET image layer) correspondingto three gated 3D PET images corresponding to respiratory phasesdifferent from each other, and three similarities may be determined. Thethree similarities may include a first similarity between the CT imagelayer and the first gated PET image layer, a second similarity betweenthe CT image layer and the second gated PET image layer, and a thirdsimilarity between the CT image layer and the third gated PET imagelayer.

In some embodiments, the similarity may include a pixel-basedsimilarity, an entropy-based similarity, a mutual informationsimilarity, a contour-based similarity, or the like, or any combinationthereof. The pixel-based similarity may be determined by comparing theCT image layer and a gated PET image layer at the pixel level. A pixelfrom the CT image layer may be compared with a corresponding pixel inthe gated PET image layer. As used herein, two pixels in two differentimages (e.g., a CT image layer and a PET image layer, two CT imagelayers, two PET image layers, etc.) are considered to correspond to eachother if they both correspond to a same spatial point. The entropy-basedsimilarity may be determined by comparing information gain between theCT image layer and the gated PET image layer. The mutual informationsimilarity may be determined by comparing mutual information between theCT image layer and the gated PET image layer. The contour-basedsimilarity may be determined by comparing a contour of an organ in theCT image layer and the corresponding gated PET image layer. The CT imagelayer and the gated PET image layer may be preprocessed respectively toextract the contour of the organ.

A similarity may be presented as a number, a percentage, a value, atext, a matrix, a determinant, or the like, or any combination thereof.For example, a similarity may be expressed as 2, 4, 5, 30, or anyinteger. In some embodiments, one or more similarities may be ranked.For example, the one or more similarities of a CT image layer withrespect to a plurality of gated PET image layers may be ranked from theminimum to the maximum, or vice versa. As another example, the motionphase determination unit 530 may analyze the similarities, and selectthe maximum similarity. The maximum similarity may indicate that thegated PET image layer is the same as or the most similar to the CT imagelayer in terms of the motion phase. For example, the gated PET imagelayer with the second respiratory phase may show the maximum similaritywith the CT image layer, indicating that this gated PET image layer isthe same as or the most similar to the CT image layer in terms of therespiratory phase.

In some embodiments, the motion phase determination unit 530 may furtherdetermine the motion phase of the CT image layer based on thesimilarities between the CT image layer and the gated PET image layers.The motion phase of the CT image layer may correspond to the subjectmotion such as the respiratory motion with a plurality of respiratoryphases, the cardiac motion with a plurality of cardiac phases, thegastrointestinal motion with a plurality of gastrointestinal phases, askeletal muscle motion with a plurality of skeletal muscle motionphases. For example, a CT image layer may correspond to a respiratoryphase. In some embodiments, a total number of respiratory phases of theCT image layers may be the same as or fewer than the number ofrespiratory phases of the gated PET image layers. If the scanning speedof a CT scanner is higher than the breathing speed of the patient, oneor more CT image layers may correspond to a same motion phase asdetermined according to the process 800.

In some embodiments, the determination of the respiratory phase of theCT image layer may be based on another respiratory signal obtained bythe gating system 180 during both PET and CT scanning. For example,during the CT scanning that provides the 3D CT image, the gating system180 may obtain the respiratory motion of the subject, and designate oneof the respiratory phases of the gated PET images for each of the CTimage layers. Respiratory phases of respiratory motion may correspond toan amplitude range of the respiratory motion. Such an amplitude range ofrespiratory motion may be reflected in a respiratory signal recordedduring a CT or PET scan. For example, the respiratory phases of thegated PET images are determined based on the amplitude range of therespiratory motion of the subject. The gating system 180 may designate arespiratory phase of a CT image layer based on the amplitude ofrespiratory signal acquired at the time of the CT scanning. Thecorrespondence between an amplitude and a respiratory phase applied inthis motion phase analysis of a CT image layer may be the same as thecorrespondence between an amplitude and a respiratory phase applied inthe gating of the PET data and/or the designation of the respiratoryphases of the gated PET images based on a respiratory signal.

In some embodiments, after the respiratory phases of the CT image layersare determined, a respiratory phase curve may be determined. Therespiratory phase curve may locate in a coordinate system with ahorizontal axis representing the layer numbers of the CT image layers,and a vertical axis representing the respiratory phases of the CT imagelayers. The respiratory phase curve may relate to the axial coordinateof the helical CT scanning that provides the 3D CT image. In someembodiments, the processing module 440 may perform analysis and/orfiltering with respect to the respiratory phase curve to reduce noisesin the matching of the CT image layer and the gated PET image layer.

In 670, the motion deformation processing unit 540 may correct CT imagelayers to generate corrected CT image layers. The corrected CT imagelayers may be used to form a corrected 3D CT image and further used toreconstruct a 3D PET image that may show information of the corrected CTimage layers and the PET data. A corrected CT image layer may be aprocessed CT image layer that corresponds to a certain respiratory phase(e.g., a reference respiratory phase). The correction may remove orreduce a difference between the processed CT image layers that are dueat least partially to that the corresponding CT image layers relate todifferent respiratory phases. The motion deformation processing unit 540may generate corrected CT image layers (or corrected 3D CT image) thatcorrespond to a same respiratory phase as each other.

For instance, for a 3D CT image including a plurality of CT imagelayers, a CT image layer corresponds to a respiratory phase. At leasttwo of the CT image layers of the 3D CT image correspond to twodifferent respiratory phases. For a 3D CT image obtained by way of a CTscan, a respiratory phase of some CT image layers may be designated as areference respiratory phase (corresponding to a reference frame of agated 3D PET image), and other CT image layers of the 3D CT image may becorrected with respect to the reference respiratory phase, and thecorrected CT image layers may constitute a corrected 3D CT imagecorresponding to the reference respiratory phase. Based on the same 3DCT image, if a different respiratory phase is designated as thereference respiratory phase, the CT image layers of the 3D CT image maybe corrected and a corrected 3D CT image corresponding this referencerespiratory phase may be generated. Accordingly, corrected 3D CT imageswith respect to different respiratory phases may be generated based onthe same 3D CT image obtained in one CT scan. Such corrected 3D CTimages corresponding to different respiratory phases may be applied inthe reconstruction of gated 3D PET images corresponding to differentrespiratory phases.

In some embodiments, the motion deformation processing unit 540 maygenerate a corrected CT image layer based on a deformation vector fieldincluding a plurality of deformation vectors. A deformation vector maybe a 2D vector, 3D vector, or an N-dimensional vector. For example, themotion deformation processing unit 540 may deform the CT image layercorresponding to a first respiratory phase into a corrected CT imagelayer corresponding to a second respiratory phase.

In some embodiments, a deformation vector field may be determined basedon a motion vector field. A deformation vector field may correspond to amotion vector field. For example, the deformation vector field for a CTimage layer from its designated respiratory phase N to a referencerespiratory phase may be determined based on the motion vector field forthe PET image layer of gated 3D PET image at the same phase. The motionvector field for the PET image layer of the Nth gated 3D PET image maybe with respect to a reference gated PET image layer of a reference PETimage (e.g., the first gated PET image). The CT image layer of phase N,the gated PET image layer of the same phase, and the reference gated PETimage layer may correspond to a same group of spatial points. Detailsabout the determination of the deformation vector field based on themotion vector field may be found elsewhere in the present disclosure.See, for example, the description regarding 910 in FIG. 9.

In 680, the reconstruction unit 550 may reconstruct a PET image based onthe corrected CT image layers and the PET data. In some embodiments, thereconstruction unit 550 may use a reconstruction algorithm to generatethe PET image. The reconstruction algorithm may include an iterativereconstruction algorithm (e.g., a statistical reconstruction algorithm),a Fourier slice theorem algorithm, a filtered back projection (FBP)algorithm, a compressed sensing (CS) algorithm, a fan-beamreconstruction algorithm, a maximum likelihood expectation maximization(MLEM) algorithm, an ordered subset expectation maximization (OSEM)algorithm, a maximum a posterior (MAP) algorithm, an analyticreconstruction algorithm, or the like, or any combination thereof.

In some embodiments, a corrected CT image may be superimposed with agated PET image so that the corrected CT image may provide structuralinformation in combination with the functional information provided bythe gated PET image.

In some embodiments, the reconstruction may further be based on thecorrected CT image layers for attenuation correction. The corrected CTimage layers may be used to generate an attenuation map applied in thePET image reconstruction. The reconstructed PET image may be a gated PETimage with the reference respiratory phase if the CT image layers arecorrected to provide corrected CT image layers corresponding to thereference respiratory phase. In some embodiments, the reconstructed PETimage may be a gated PET image with any of the respiratory phasesconsidering that any one of the respiratory phases may be designated asthe reference respiratory phase. By way of the attenuation correction,attenuation artifacts in the PET image may be decreased. The attenuationartifacts in the PET image may be caused by attenuation of photon rays(e.g., γ rays) when they pass through the subject (e.g., a patient). Forexample, a positron emitted by an imaging agent taken by a patientencountering an electron from a tissue of the patient may annihilate. Apair of gamma photons may be generated in response to the annihilation.When the photons pass through the tissue to reach the detector 112(e.g., a PET detector), at least a portion of the photons may reach thedetector 112, and the rest of the photons may be scattered or absorbedby the tissue of the patient. The photons scattered or absorbed maycause the attenuation of the photon ray which in turn may contribute tothe attenuation artifacts in the PET image.

In some embodiments, the attenuation artifacts may be corrected by wayof the attenuation correction that may include the application of atleast one of attenuation coefficient. Merely by way of example, theattenuation coefficient may be a tissue attenuation coefficientcorresponding to the γ ray in an energy level of 511 KeV. The tissueattenuation coefficient may be used to determine an attenuationcorrection factor of the γ ray. For instance, the attenuation correctionfactor may be determined by formula (2):ACF=e ^(∫u(x)dx),  (2)where ACF represents the attenuation correction factor of the γ ray, andu represents the tissue attenuation coefficient.

In some embodiments, the attenuation correction factor may be determinedaccording to the corrected CT image layers. For example, a tissueattenuation coefficient corresponding to the X ray may be determinedbased on the corrected CT image layer. The tissue attenuationcoefficient corresponding to the X ray may be transformed into thetissue attenuation coefficient corresponding to the γ ray, and thetissue attenuation coefficient corresponding to the γ ray may be used todetermine the tissue attenuation correction factor of the γ ray usingthe formula (2).

It is understood that the operations on a 3D CT image described hereinincluding, for example, motion phase determination, correction, etc.,may be performed on a 3D attenuation map corresponding to the 3D CTimage. For instance, a 3D attenuation map including a plurality of 2Dattenuation map layers may be generated based on the 3D CT image;subsequently, one or more operations including, for example, motionphase determination, correction, etc., may be performed on the 3Dattenuation map or one or more of the plurality of 2D attenuation maplayers of the 3D attenuation map to generate a processed 3D attenuationmap, e.g., a corrected 3D attenuation map. In some embodiments, a 3Dattenuation map may be generated by processing or modifying, based on analgorithm, voxel values of the voxels of the 3D CT image. A voxel valueof a voxel of the 3D attenuation map may relate to the voxel value ofthe corresponding voxel of the 3D CT image based on the conversionalgorithm.

FIG. 6B is a flowchart illustrating an exemplary process forreconstructing a PET image according to some embodiments of the presentdisclosure. The operations of 611 through 666 may be similar to theoperations of 610 through 660 respectively.

In 667, for each respiratory phase of the plurality of CT images, theprocessing module 440 may generate a corrected 3D CT image correspondingto the respiratory phase based on deformation vector fields of the CTimages. As described in 667, each CT image (or CT image layer of a 3D CTimage) may correspond to its respiratory phase. Any one of therespiratory phases may be determined as the reference phase that CTimages with other respiratory phases may be corrected based on adeformation vector corresponding to the reference phase. Relatedescription may be found in the description of FIG. 6A. The processingmodule may generating the corrected 3D CT image with respect to therespiratory phase based on the corrected CT images. Therefore, 3D CTimages may be generated corresponding to each respiratory phase, andfurther corresponding to a group of gated PET data with the samerespiratory phase.

In 668, the processing module 440 may reconstruct gated PET images basedon the gated PET data and the corrected 3D CT images. Since therelationship between the corrected 3D CT image and a group of gated PETdata are determined in 677, the reconstruction unit 550 may reconstructthe gated PET images based on the groups of gated PET data andcorresponding corrected 3D CT images.

FIG. 7 is a flowchart illustrating an exemplary process 700 for gatingPET data according to some embodiments of the present disclosure. Insome embodiments, process 700 may include obtaining a respiration signalof the subject during the scanning, gating the PET data based on therespiration signal, reconstructing the gated PET data to obtain theplurality of gated PET images corresponding to different respiratoryphases of the subject. In some embodiments, one or more operations ofprocess 700 for gating PET data may be implemented in the imageprocessing system 100 illustrated in FIG. 1. For example, the process700 may be stored in the storage module 420 of the data processingsystem 130 in the form of instructions, and invoked and/or executed bythe data processing system 130 (e.g., the processor 230 of the dataprocessing system 130).

In 710, the gating unit 510 may obtain the respiration signal of thesubject during the scanning. In some embodiments, the respiratory signalmay be obtained from the gating system 180. The gating system 180 may beused to collect information such as breathing information, heartbeatinformation etc., and analyze the information to obtain the gatingparameter (e.g., the respiratory phase). In some embodiments, therespiratory signal may be approximated as described with respect to 630.

In 720, the gating unit 510 may gate the PET data based on therespiration signal. The respiratory signal may correspond to differentrespiratory phases of the subject. For example, the respiratory signalmay correspond to N respiratory phases, wherein the N may be 1, 2, 5, 8,or any integer greater than 1. For example, the gating unit 510 maydivide the respiratory signal into 4 respiratory phases, each of whichmay correspond to a different part in a cycle of the respiratory signal.The PET data may be gated or divided into groups (or frames) of gatedPET data based on the 4 respiratory phases. Each group of gated PET datamay correspond to one of the four respiratory phases.

In 730, the reconstruction unit 550 may reconstruct a plurality of gatedPET image based on the gated PET data. In some embodiments, thereconstruction unit 550 may use a reconstruction algorithm to generatethe plurality of gated PET images. Exemplary reconstruction algorithmsmay include an iterative reconstruction algorithm (e.g., a statisticalreconstruction algorithm), a Fourier slice theorem algorithm, a filteredback projection (FBP) algorithm, a compressed sensing (CS) algorithm, afan-beam reconstruction algorithm, a maximum likelihood expectationmaximization (MLEM) algorithm, an ordered subset expectationmaximization (OSEM) algorithm, a maximum a posterior (MAP) algorithm, ananalytic reconstruction algorithm, or the like, or any combinationthereof. For example, the reconstruction unit 550 may generate theplurality of gated PET images using the statistical reconstructionalgorithm or the MLEM algorithm.

In some embodiments, the number of the gated PET images corresponding toa respiratory cycle may equal the number of the respiratory phases. Forexample, if the respiratory signal is gated into 4 respiratory phases ina respiratory cycle, the PET data corresponding to a respiratory cyclemay be divided into 4 groups of gated PET data, and the reconstructionunit 550 may reconstruct the 4 groups of gated PET data into 4 gated PETimages corresponding to the 4 different respiratory phases.

FIG. 8 is a flowchart illustrating an exemplary process 800 fordetermining a respiratory phase of a CT image layer according to someembodiments of the present disclosure. As shown in FIG. 8, the process800 may include determining similarities between a CT image layer and aplurality of gated PET image layers, identifying the maximum similarityamong the determined similarities, and determining a respiratory phaseof the CT image layer. In some embodiments, one or more operations ofprocess 800 for determining a respiratory phase of the CT image layermay be implemented in the image processing system 100 illustrated inFIG. 1. For example, the process 800 may be stored in the storage module420 of the data processing system 130 in the form of instructions, andinvoked and/or executed by the data processing system 130 (e.g., theprocessor 230 of the data processing system 130).

In 810, the motion phase determination unit 530 may determinesimilarities between a CT image layer and a plurality of gated PET imagelayers of a subject. In some embodiments, the similarity may include apixel-based similarity, an entropy-based similarity, a mutualinformation similarity, or the like, or any combination thereof. Thegated PET images may be 3D images with a same FOV as the 3D CT image.The 3D CT image and a gated 3D PET image may be expressed in a samecoordinate system to facilitate the determination of the similarity of aCT image layer and a gated PET image layer there between. The samecoordinate system may include an X axis, a Y axis, and a Z axis. The 3DCT image and the gated PET image may be expressed as C(x,y,z) andP(x,y,z,t), respectively, where t represents respiratory phase of thegated PET image. The coordinate (x,y,z) and the coordinate (x,y,z,t) maycorrespond to a same region of interest (ROI). For instance, the ROI maybe the chest of the subject, and determined by a CT scanning system anda PET scanning system.

In some embodiments, the motion phase determination unit 530 maydetermine the mutual information similarity between each layer of the 3DCT image (e.g., an axial slice in the transverse plane of the 3D CTimage) and a corresponding layer (e.g., also an axial slice in thetransverse plane) of each of a plurality of gated PET images based onthe function shown as formula (3):

$\begin{matrix}{{{\tau(z)} = {\underset{g}{argmax}\left( {D\left( {{C\left( {x,y,z} \right)},{P\left( {x,y,z,g} \right)}} \right)} \right)}},} & (3)\end{matrix}$where τ(z) represents the respiratory gate of a 3D CT image at slice z,D represents a measurement of similarity between the 3D CT image and thegated PET image, C(x,y,z) represents a 3D CT image, P(x,y,z,g)represents a gated PET image, x, y, and z represent coordinates ofvoxels in the 3D CT image and the gated PET image, and g represents arespiratory phase of the gated PET image. For a certain layer of the 3DCT image, the value of z is determined. The value of z of acorresponding layer of the gated PET image may be designated as the sameof the CT image. Therefore, the 3D CT image C(x,y,z) and the gated PETimage P(x,y,z,g) may be expressed as 2D images, and represented as a 2DCT image (or CT image layer) C(z), a gated PET image layer P(z,g). Insome embodiments, z may be a value range that represents a cycle of thehelical scanning of the CT scanning. During a cycle of the CT scanning,the radioactive scanning source 115 and the detector 112 may rotate 360degrees. The C(x,y,z) may represent a 3D CT image as a portion of thewhole 3D CT image. Correspondingly, the P(x,y,z,g) may represent aportion of the gated 3D PET image.

In some embodiments, the measurement of similarity D between the CTimage layer and the gated PET image layer may be expressed as formula(4):D(C(z),p(z,t))=H(C(z))+H(p(z,g))−H(C(z), p(z,g)),  (4)where D(C(z), P(z,g)) represents mutual information between the CT imagelayer and the gated PET image layer, H(C(z)) represents an entropy ofthe CT image layer, H(P(z,g)) represents an entropy of the gated PETimage layer, and H(C(z), P(z,g)) represents a combined entropy of the CTimage layer and the gated PET image layer. In some embodiments, theentropy of the CT image H(C(z)), or the entropy of the gated PET imageH(P(z,g)) may be determined by formula (5):H(A)=−∫₀ ^(+∞) p _(A)(v)log(p _(A)(v))dv,  (5)where H(A) represents the entropy of the CT image layer H(C(z)), or theentropy of the gated PET image layer H(P(z,g)), A represent an image, vrepresents an image pixel value in image A, and p_(A)(v) represent ahistogram of the image A. In some embodiments, p_(A)(v) is determined byformula (6):p _(A)(v)=∫∫_(All)δ(A(x,y)−v)dxdy,  (6)where A(x,y) is the pixel value at (x,y), δ represents a window functioncentered at 0 (e.g., a Gaussian function with mean 0). The pixel valueat (x,y) may be the gray value of the pixel at (x,y).

In some embodiments, the combined entropy of the gated PET image layerH(C(z),P(z,g)) may be determined by formula (7):H(A,B)=−∫∫₀ ^(+∞) p _(A,B)(v,u))log(p _(A,B)(v,u)dudv,  (7)where A and B represent two images, respectively. H(A,B) represents thecombined entropy of image A and image B, u represents image pixel valuein image A, v represents an image pixel value in image B, andp_(A,B)(v,u) is a combined histogram of image A and image B, and may bedetermined by formula (8):p _(A,B)(v,u)=∫∫_(All)δ(A(x,y)−v)δ(B(x,y)−u)dxdy,  (8)where δ represents a window function centered at 0. In some embodiments,the function δ in the formula (6) and the formula (8) may take the formof the Dirac delta function, as determined by formulae (9) and (10):

$\begin{matrix}{{\delta(x)} = \left\{ {\begin{matrix}{{+ \infty},} & {x = 0} \\{0,} & {x \neq 0}\end{matrix}.} \right.} & (9)\end{matrix}$which is constrained to satisfy the identity:∫_(−∞) ^(+∞)δ(x)dx=1.  (10)

In 820, the motion phase determination unit 530 may identify a highestor maximum similarity among the determined similarities. In someembodiments, the motion phase determination unit 530 may rank thesimilarities from the lowest similarity to the highest similarity. Thehighest similarity may be identified by the motion phase determinationunit 530. For example, the determined similarities may include 0.6, 0.5,0.3, and 0.9. The motion phase determination unit 530 may rank thesimilarities as 0.3, 0.5, 0.6, and 0.9, and identify 0.9 as the highestsimilarity.

In 830, the motion phase determination unit 530 may further determine arespiratory phase of the CT image layer based on the highest similarityand the corresponding gated PET image layer that exhibits the highestsimilarity with the CT image layer. In some embodiments, the respiratoryphase of the corresponding gated PET image layer that has the highestsimilarity with the CT image layer may be designated as the respiratoryphase of the CT image layer.

FIG. 9 is a flowchart illustrating an exemplary process 900 forcorrecting a CT image layer according to some embodiments of the presentdisclosure. As shown in FIG. 9, the process 900 may include determininga deformation vector field of a CT image layer based on a motion vectorfield, and correcting the CT image layer with respect to deformationbased on the deformation vector field. In some embodiments, one or moreoperations of process 900 for correcting a CT image layer may beimplemented in the image processing system 100 illustrated in FIG. 1.For example, the process 900 may be stored in the storage module 420 ofthe data processing system 130 in the form of instructions, and invokedand/or executed by the data processing system 130 (e.g., the processor230 of the data processing system 130).

In 910, the motion deformation processing unit 540 may determine adeformation vector field of a CT image layer based on a motion vectorfield and the determined respiratory phase of the CT image layer. Thedeformation vector field may include a plurality of deformation vectors.A deformation vector may be a 2D vector, 3D vector, or an N-dimensionalvector. In some embodiments, the deformation vector field of the CTimage layer may be determined based on a motion vector field of a gatedPET image layer that has the same respiration phase with the CT imagelayer.

In some embodiments, the deformation vector field may be determinedbased on the motion vector field of the corresponding gated PET imagethat includes the gated PET layer having the highest similarity with CTimage layer. The motion vector filed from respiratory phase t toreference frame may be expressed as(m_(u)(x,y,z,t),m_(v)(x,y,z,t),m_(w)(x,y,z,t)), where m_(u) representsthe motion vector component in the x axis direction, m_(v) representsthe motion vector component in the y axis direction, m_(w) representsthe motion vector component in the z axis direction, and t representsthe respiratory phase. The deformation vector field of the 3D CT imageat slice z may be expressed as(m_(u)(x,y,z,τ(z)),m_(v)(x,y,z,τ(z)),m_(w)(x,y,z,τ(z))), wherem_(u)(x,y,z,τ(z)) represents the deformation vector component in the xaxis direction, m_(v)(x,y,z,τ(z)) represents the deformation vectorcomponent in the y axis direction, m_(w)(x,y,z,τ(z)) represents thedeformation vector component in the in the z axis direction, and τ(z)represents the respiratory phase of the CT image layer. For example, therespiration phase of the CT image layer τ(z) may correspond to a 4threspiratory phase of the corresponding gated PET image layer by theformula (3). The deformation vector field of the CT image layer may bedetermined as (m_(u)(x,y,z,4), m_(v)(x,y,z,4), m_(w)(x,y,z,4)) that maybe the same as the motion vector field of the corresponding gated PETimage layer relative to the reference gated PET image corresponding to areference motion phase (e.g., the gated PET image corresponding to afirst respiratory phase of a respiratory motion) as described elsewherein the present disclosure. The deformation vector field of the CT imagelayer so determined is also considered relative to the same referencemotion phase.

In 920, the motion deformation processing unit 540 may correct the CTimage layer with respect to deformation based on the deformation vectorfield. In some embodiment, the respiratory phase of the CT image layermay be the same as the respiratory phase of the gated PET image layerhaving the highest similarity with the CT image layer. The motiondeformation processing unit 540 may correct the CT image layer based onthe determined deformation vector field, and generate the corrected CTimage layer using formula (11):C _(ref)(x,y,z)=C(x+m _(u)(x,y,z,τ(z)),y+m _(v)(x,y,z,τ(z)),z+m_(w)(x,y,z,τ(z))),  (11)where C_(ref)(x,y,z) represents the corrected CT image layer at thereference frame.

EXAMPLES

The examples are provided for illustration purposes, and not intended tolimit the scope of the present disclosure.

Example 1

FIG. 10A and FIG. 10B illustrate exemplary CT image layers withartifacts according to some embodiments of the present disclosure. FIG.10A and FIG. 10B show, respectively, a coronal slice and a sagittalslice of a 3D CT image obtained by scanning the thorax of a patient. Thepatient lied on the back and breathed freely during the scan. Asillustrated, the coronal slice of the 3D CT image in FIG. 10A showsartifacts 1010, and the sagittal slice in FIG. 10B shows artifacts 1020.The artifacts may be caused by a shift of the lung and liver of thesubject due to respiratory motion during the CT scan. The artifact 1010and artifact 1020 are located at an upper part of the liver. Theartifact 1010 and/or artifact 1020 may decrease the quality of the CTimage and the PET image reconstructed with this CT image for attenuationcorrection, hence affecting the diagnosis of the patient by a user(e.g., a doctor).

Example 2

FIG. 11A-1 through FIG. 11A-3 and FIG. 11B-1 through FIG. 11B-3illustrate gated PET images of two different respiratory phasesreconstructed overlapped with the same attenuation map withoutcorrection according to some embodiments of the present disclosure. Thetwo gated PET images correspond to two different respiratory phases, anend-inspiratory phase (EIP) and an end-expiratory phase (EEP). Theattenuation map corresponds to a 3D CT image without correction obtainedfrom a helical CT scan during which the subject breathed normally.

FIG. 11A-1 through FIG. 11A-3 correspond to the EEP. FIG. 11B-1 throughFIG. 11B-3 correspond to the EIP. FIG. 11A-1 and FIG. 11B-1 aretransverse slices of the gated PET images corresponding to the EEP andthe EIP, respectively. FIG. 11A-2 and FIG. 11B-2 are coronal slices ofthe gated PET images corresponding to the EEP and the EIP, respectively.FIG. 11A-3 and FIG. 11B-3 are sagittal slices of the gated PET imagescorresponding to the EEP and the EIP, respectively.

As shown in the FIG. 11A-1 through FIG. 11A-3, the dome of the livermatches well with the attenuation map, while there is a mismatch at thelower part of the liver between the PET image and the attenuation mapindicated by 1110 and 1120. As shown in the FIG. 11B-1 through FIG.11B-3, the lower part of the liver matches well with the attenuationmap, while there is a mismatch at the top of the liver between the PETimage and the attenuation map indicated by 1130, 1140, and 1150.

Example 3

FIG. 12 illustrate the results of slice-wise respiratory phasedetermination on an attenuation map obtained from an uncorrected 3D CTimage according to some embodiments of the present disclosure. FIG. 12shows a coronal slice of the attenuation map. Different transversalimage layers are labeled as 1210, 1220, 1230, 1240, 1250 . . . 1290.Respiratory gate number (also referred as the phase number) for theimage layers may be obtained by determining image similarity between thetransversal image layers with corresponding layers in a gated PET imageas described in 660. Each of the CT image layers 1210 through 1290corresponds to a gate number representing a respiratory phase. The CTimage layers 1210 and 1290 correspond to Gate 1. The CT image layers1220 and 1280 correspond to Gate 2. The CT image layers 1230 and 1270correspond to Gate 3. The CT image layers 1240 and 1260 correspond toGate 4. The CT image layer 1250 corresponds to Gate 5. A same gatingnumber in FIG. 12 indicates a same motion amplitude (or a same range ofmotion amplitude) occurring in an inspiratory phase or in an expiratoryphase.

Example 4

FIG. 13A-1 through FIG. 13A-3 and FIG. 13B-1 through FIG. 13B-3illustrate a gated PET image overlapped with an attenuation map withoutcorrection and with a corrected attenuation map, respectively, accordingto some embodiments of the present disclosure. FIG. 13A-1 is a slice ofthe gated PET image and attenuation map in the transverse plane. FIG.13A-2 is a slice of the gated PET image and attenuation map in thecoronal plane. FIG. 13A-3 is a slice of the gated PET image andattenuation map in the sagittal plane. As shown in FIG. 13A-1 throughFIG. 13A-3, the dome of the liver does not match the attenuation map.See, e.g., the mismatch indicated by 1310, 1320, 1330, 1340, and 1350.

FIG. 13A-1 through FIG. 13A-3 and FIG. 13B-1 through FIG. 13B-3illustrate a gated PET image overlapped with an attenuation map withoutcorrection and with a corrected attenuation map, respectively, accordingto some embodiments of the present disclosure. The corrected 3D CT imageused in FIG. 13B-1 through FIG. 13B-3 and the 3D CT image withoutcorrection used in FIG. 13A-1 through FIG. 13A-3 were based on data froma same CT scan. FIG. 13B-1 is a slice of the gated PET image andattenuation map in the transverse plane. FIG. 13B-2 is a slice of thegated PET image and attenuation map in the coronal plane. FIG. 13B-3 isa slice of the gated PET image and attenuation map in the sagittalplane. As show in the FIG. 13B-1 through FIG. 13B-3, the mismatch of thegated PET image and the CT image was reduced, compared to that shown inFIG. 13A-1 through FIG. 13A-3.

Having thus described the basic concepts, it may be rather apparent tothose skilled in the art after reading this detailed disclosure that theforegoing detailed disclosure is intended to be presented by way ofexample only and is not limiting. Various alterations, improvements, andmodifications may occur and are intended to those skilled in the art,though not expressly stated herein. These alterations, improvements, andmodifications are intended to be suggested by this disclosure, and arewithin the spirit and scope of the exemplary embodiments of thisdisclosure.

Moreover, certain terminology has been used to describe embodiments ofthe present disclosure. For example, the terms “one embodiment,” “anembodiment,” and/or “some embodiments” mean that a particular feature,structure or characteristic described in connection with the embodimentis included in at least one embodiment of the present disclosure.Therefore, it is emphasized and should be appreciated that two or morereferences to “an embodiment” or “one embodiment” or “an alternativeembodiment” in various portions of this specification are notnecessarily all referring to the same embodiment. Furthermore, theparticular features, structures or characteristics may be combined assuitable in one or more embodiments of the present disclosure.

Further, it will be appreciated by one skilled in the art, aspects ofthe present disclosure may be illustrated and described herein in any ofa number of patentable classes or context including any new and usefulprocess, machine, manufacture, or composition of matter, or any new anduseful improvement thereof. Accordingly, aspects of the presentdisclosure may be implemented entirely hardware, entirely software(including firmware, resident software, micro-code code, etc.) orcombining software and hardware implementation that may all generally bereferred to herein as a “block,” “module,” “module,” “unit,”“component,” or “system.” Furthermore, aspects of the present disclosuremay take the form of a computer program product embodied in one or morecomputer readable media having computer readable program code embodiedthereon.

A computer readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including electro-magnetic, optical, or thelike, or any suitable combination thereof. A computer readable signalmedium may be any computer readable medium that is not a computerreadable storage medium and that may communicate, propagate, ortransport a program for use by or in connection with an instructionexecution system, apparatus, or device. Program code embodied on acomputer readable signal medium may be transmitted using any appropriatemedium, including wireless, wireline, optical fiber cable, RF, or thelike, or any suitable combination of the foregoing.

Computer program code for carrying out operations for aspects of thepresent disclosure may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C++, C#, VB. NET,Python or the like, conventional procedural programming languages, suchas the “C” programming language, Visual Basic, Fortran 2003, Perl, COBOL2002, PHP, ABAP, dynamic programming languages such as Python, Ruby andGroovy, or other programming languages. The program code may executeentirely on the user's computer, partly on the user's computer, as astand-alone software package, partly on the user's computer and partlyon a remote computer or entirely on the remote computer or server. Inthe latter scenario, the remote computer may be connected to the user'scomputer through any type of network, including a local area network(LAN) or a wide area network (WAN), or the connection may be made to anexternal computer (for example, through the Internet using an InternetService Provider) or in a cloud computing environment or offered as aservice such as a Software as a Service (SaaS).

Furthermore, the recited order of processing elements or sequences, orthe use of numbers, letters, or other designations therefore, is notintended to limit the claimed processes and methods to any order exceptas may be specified in the claims. Although the above disclosurediscusses through various examples what is currently considered to be avariety of useful embodiments of the disclosure, it is to be understoodthat such detail is solely for that purpose, and that the appendedclaims are not limited to the disclosed embodiments, but, on thecontrary, are intended to cover modifications and equivalentarrangements that are within the spirit and scope of the disclosedembodiments. For example, although the implementation of variouscomponents described above may be embodied in a hardware device, it mayalso be implemented as a software only solution—e.g., an installation onan existing server or mobile device.

Similarly, it should be appreciated that in the foregoing description ofembodiments of the present disclosure, various features are sometimesgrouped together in a single embodiment, figure, or description thereoffor the purpose of streamlining the disclosure aiding in theunderstanding of one or more of the various inventive embodiments. Thismethod of disclosure, however, is not to be interpreted as reflecting anintention that the claimed subject matter requires more features thanare expressly recited in each claim. Rather, inventive embodiments liein less than all features of a single foregoing disclosed embodiment.

In some embodiments, the numbers expressing quantities of ingredients,properties such as molecular weight, reaction conditions, and so forth,used to describe and claim certain embodiments of the application are tobe understood as being modified in some instances by the term “about,”“approximate,” or “substantially.” For example, “about,” “approximate,”or “substantially” may indicate ±20% variation of the value itdescribes, unless otherwise stated. Accordingly, in some embodiments,the numerical parameters set forth in the written description andattached claims are approximations that may vary depending upon thedesired properties sought to be obtained by a particular embodiment. Insome embodiments, the numerical parameters should be construed in lightof the number of reported significant digits and by applying ordinaryrounding techniques. Notwithstanding that the numerical ranges andparameters setting forth the broad scope of some embodiments of theapplication are approximations, the numerical values set forth in thespecific examples are reported as precisely as practicable.

Each of the patents, patent applications, publications of patentapplications, and other material, such as articles, books,specifications, publications, documents, things, and/or the like,referenced herein is hereby incorporated herein by this reference in itsentirety for all purposes, excepting any prosecution file historyassociated with same, any of same that is inconsistent with or inconflict with the present document, or any of same that may have alimiting affect as to the broadest scope of the claims now or laterassociated with the present document. By way of example, should there beany inconsistency or conflict between the description, definition,and/or the use of a term associated with any of the incorporatedmaterial and that associated with the present document, the description,definition, and/or the use of the term in the present document shallprevail.

In closing, it is to be understood that the embodiments of theapplication disclosed herein are illustrative of the principles of theembodiments of the application. Other modifications that may be employedmay be within the scope of the application. Thus, by way of example, butnot of limitation, alternative configurations of the embodiments of theapplication may be utilized in accordance with the teachings herein.Accordingly, embodiments of the present application are not limited tothat precisely as shown and described.

We claim:
 1. An imaging method implemented on at least one machine eachof which has at least one processor and storage, the method comprising:obtaining a 3D CT image of a scanning area of a subject; generating anattenuation map by processing voxel values of a plurality of voxels ofthe 3D CT image, a voxel value of a voxel of the attenuation maprelating to a voxel value of a corresponding voxel of the 3D CT image;obtaining PET data of the scanning area of the subject, the PET datacorresponding to a first motion signal with a plurality of motion phasesof the subject; gating the PET data based on the plurality of motionphases of the first motion signal; reconstructing, based on the gatedPET data, a plurality of gated 3D PET images, a gated 3D PET imagecorresponding to one of the plurality of motion phases, a gated 3D PETimage including a plurality of gated PET image layers, a gated PET imagelayer corresponding to a group of spatial points relating to thesubject; determining, based on the plurality of gated 3D PET images, amotion vector field corresponding to a gated 3D PET image of theplurality of gated 3D PET images, a motion vector field corresponding toa motion phase; determining a plurality of motion phases for theattenuation map based on the motion phases of the plurality of gated 3DPET images; correcting the attenuation map based on a plurality ofmotion vector fields of the plurality of gated 3D PET images; andreconstructing a gated PET image based on the corrected attenuation mapand the PET data.
 2. The method of claim 1, wherein the determining,based on the plurality of gated 3D PET images, a motion vector fieldcorresponding to a gated 3D PET image of the plurality of gated 3D PETimages comprises: registering the plurality of gated 3D PET images witha reference 3D PET image; and determining, based on the registration, amotion vector field corresponding to a gated 3D PET image of theplurality of gated 3D PET images.
 3. The method of claim 2, wherein theregistering the plurality of gated 3D PET images with a reference 3D PETimage is based on at least one of an optical flow registrationalgorithm, demons registration algorithm, or a B-spline registrationalgorithm.
 4. The method of claim 1, wherein the attenuation mapincludes a plurality of attenuation map layers, an attenuation map layercorresponding to a group of spatial points relating to the subject, andthe determining a plurality of motion phases for the attenuation mapbased on the motion phases of the plurality of gated 3D PET imagescomprises: for each of the plurality of attenuation map layers,identifying, from each of the plurality of gated 3D PET images, a gatedPET image layer corresponding to a same group of spatial points as theattenuation map layer; determining a similarity between the attenuationmap layer and each of the plurality of identified gated PET imagelayers; and designating one of the motion phases of the plurality ofgated 3D PET images as the motion phase of the attenuation map layerbased on its similarities with the plurality of identified gated PETimage layers.
 5. The method of claim 4, wherein the designating one ofthe motion phases of the plurality of gated 3D PET images as the motionphase of the attenuation map layer based on its similarities with theplurality of identified gated PET image layers comprises: identifying ahighest similarity among the determined similarities between theattenuation map layer and the plurality of identified gated PET imagelayers; and designating the motion phase of the gated 3D PET imageincluding the identified gated PET image layer having the highestsimilarity as the motion phase of the attenuation map layer.
 6. Themethod of claim 4, wherein the determining the similarity between theattenuation map layer and each of the plurality of identified gated PETimage layers is based on at least one of a pixel-based similarity, anentropy-based similarity, a mutual information similarity, or acontour-based similarity.
 7. The method of claim 1, wherein thedetermining a plurality of motion phases for the attenuation map basedon the motion phases of the plurality of gated 3D PET images comprises:obtaining a second motion signal during a scan that provides the 3D CTimage, wherein the second motion signal is of a same type or transformedto a same type as the first motion signal; and determining the pluralityof motion phases of the attenuation map based on the motion phases ofthe plurality of gated 3D PET images and the second motion signal. 8.The method of claim 7, wherein the second motion signal is obtained froman external device.
 9. The method of claim 1, wherein the plurality ofmotion phases of the first motion signal are determined based on anamplitude or a time interval of a motion presented in the first motionsignal.
 10. The method of claim 1, wherein the first motion signal isobtained based on the PET data or from an external device.
 11. Themethod of claim 1, wherein the attenuation map includes a plurality ofattenuation map layers, and the correcting the attenuation map includes:for each of the plurality of attenuation map layers, determining adeformation vector field for the attenuation map layer based on themotion vector field of the gated 3D PET image corresponding to the samemotion phase as the attenuation map layer with respect to a gated 3D PETimage corresponding to a reference motion phase; and correcting theattenuation map layer with respect to the reference motion phase basedon the deformation vector field.
 12. The method of claim 11, wherein thereference motion phase is one of the plurality of motion phases of thesubject.
 13. The method of claim 1, wherein the motion vector fieldincludes a plurality of motion vectors, the motion vector representing amotion of a spatial point of the subject from a gated 3D PET image toanother gated 3D PET image.
 14. A system comprising: at least onestorage device storing a set of instructions; at least one processor incommunication with the at least one storage device, wherein whenexecuting the set of instructions, the at least one processor isconfigured to cause the system to perform operations including:obtaining a 3D CT image of a scanning area of a subject; generating anattenuation map by processing voxel values of a plurality of voxels ofthe 3D CT image, a voxel value of a voxel of the attenuation maprelating to a voxel value of a corresponding voxel of the 3D CT image;obtaining PET data of the scanning area of the subject, the PET datacorresponding to a first motion signal with a plurality of motion phasesof the subject; gating the PET data based on the plurality of motionphases of the first motion signal; reconstructing, based on the gatedPET data, a plurality of gated 3D PET images, a gated 3D PET imagecorresponding to one of the plurality of motion phases, a gated 3D PETimage including a plurality of gated PET image layers, a gated PET imagelayer corresponding to a group of spatial points relating to thesubject; determining, based on the plurality of gated 3D PET images, amotion vector field corresponding to a gated 3D PET image of theplurality of gated 3D PET images, a motion vector field corresponding toa motion phase; determining a plurality of motion phases for theattenuation map based on the motion phases of the plurality of gated 3DPET images; correcting the attenuation map based on a plurality ofmotion vector fields of the plurality of gated 3D PET images; andreconstructing a gated PET image based on the corrected attenuation mapand the PET data.
 15. The system of claim 14, wherein the determining,based on the plurality of gated 3D PET images, a motion vector fieldcorresponding to a gated 3D PET image of the plurality of gated 3D PETimages comprises: registering the plurality of gated 3D PET images witha reference 3D PET image; and determining, based on the registration, amotion vector field corresponding to a gated 3D PET image of theplurality of gated 3D PET images.
 16. The system of claim 14, whereinthe attenuation map includes a plurality of attenuation map layers, anattenuation map layer corresponding to a group of spatial pointsrelating to the subject, and the determining a plurality of motionphases for the attenuation map based on the motion phases of theplurality of gated 3D PET images comprises: for each of the plurality ofattenuation map layers, identifying, from each of the plurality of gated3D PET images, a gated PET image layer corresponding to a same group ofspatial points as the attenuation map layer; determining a similaritybetween the attenuation map layer and each of the plurality ofidentified gated PET image layers; and designating one of the motionphases of the plurality of gated 3D PET images as the motion phase ofthe attenuation map layer based on its similarities with the pluralityof identified gated PET image layers.
 17. The system of claim 14,wherein the determining a plurality of motion phases for the attenuationmap based on the motion phases of the plurality of gated 3D PET imagescomprises: obtaining a second motion signal during a scan that providesthe 3D CT image, wherein the second motion signal is of a same type ortransformed to a same type as the first motion signal; and determiningthe plurality of motion phases of the attenuation map based on themotion phases of the plurality of gated 3D PET images and the secondmotion signal.
 18. The system of claim 14, wherein the attenuation mapincludes a plurality of attenuation map layers, and the correcting theattenuation map includes: for each of the plurality of attenuation maplayers, determining a deformation vector field for the attenuation maplayer based on the motion vector field of the gated 3D PET imagecorresponding to the same motion phase as the attenuation map layer withrespect to a gated 3D PET image corresponding to a reference motionphase; and correcting the attenuation map layer with respect to thereference motion phase based on the deformation vector field.
 19. Thesystem of claim 18, wherein the reference motion phase is one of theplurality of motion phases of the subject.
 20. A non-transitorycomputer-readable storage medium, comprising at least one set ofinstructions, wherein when executed by at least one processor of acomputing device, the at least one set of instructions direct the atleast one processor to perform operations including: obtaining a 3D CTimage of a scanning area of a subject; generating an attenuation map byprocessing voxel values of a plurality of voxels of the 3D CT image, avoxel value of a voxel of the attenuation map relating to a voxel valueof a corresponding voxel of the 3D CT image; obtaining PET data of thescanning area of the subject, the PET data corresponding to a firstmotion signal with a plurality of motion phases of the subject; gatingthe PET data based on the plurality of motion phases of the first motionsignal; reconstructing, based on the gated PET data, a plurality ofgated 3D PET images, a gated 3D PET image corresponding to one of theplurality of motion phases, a gated 3D PET image including a pluralityof gated PET image layers, a gated PET image layer corresponding to agroup of spatial points relating to the subject; determining, based onthe plurality of gated 3D PET images, a motion vector fieldcorresponding to a gated 3D PET image of the plurality of gated 3D PETimages, a motion vector field corresponding to a motion phase;determining a plurality of motion phases for the attenuation map basedon the motion phases of the plurality of gated 3D PET images; correctingthe attenuation map based on a plurality of motion vector fields of theplurality of gated 3D PET images; and reconstructing a gated PET imagebased on the corrected attenuation map and the PET data.